two version of R2R are here HEAD master

108 files changed, 23457 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/__init__.py
new file mode 100644
index 00000000..fdf8caba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/__init__.py
@@ -0,0 +1,5 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/_input_output_helpers.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/_input_output_helpers.py
new file mode 100644
index 00000000..1a13ab41
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/_input_output_helpers.py
@@ -0,0 +1,427 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import collections.abc
+import re
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    CustomModelJobInput as RestCustomModelJobInput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    CustomModelJobOutput as RestCustomModelJobOutput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import InputDeliveryMode
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobInput as RestJobInput
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobInputType
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobOutput as RestJobOutput
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobOutputType, LiteralJobInput
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    MLFlowModelJobInput as RestMLFlowModelJobInput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    MLFlowModelJobOutput as RestMLFlowModelJobOutput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    MLTableJobInput as RestMLTableJobInput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    MLTableJobOutput as RestMLTableJobOutput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import OutputDeliveryMode
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    TritonModelJobInput as RestTritonModelJobInput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    TritonModelJobOutput as RestTritonModelJobOutput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    UriFileJobInput as RestUriFileJobInput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    UriFileJobOutput as RestUriFileJobOutput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    UriFolderJobInput as RestUriFolderJobInput,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    UriFolderJobOutput as RestUriFolderJobOutput,
+)
+from azure.ai.ml._utils.utils import is_data_binding_expression
+from azure.ai.ml.constants import AssetTypes, InputOutputModes, JobType
+from azure.ai.ml.constants._component import IOConstants
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._job.input_output_entry import InputOutputEntry
+from azure.ai.ml.entities._util import normalize_job_input_output_type
+from azure.ai.ml.exceptions import (
+    ErrorCategory,
+    ErrorTarget,
+    JobException,
+    ValidationErrorType,
+    ValidationException,
+)
+
+INPUT_MOUNT_MAPPING_FROM_REST = {
+    InputDeliveryMode.READ_WRITE_MOUNT: InputOutputModes.RW_MOUNT,
+    InputDeliveryMode.READ_ONLY_MOUNT: InputOutputModes.RO_MOUNT,
+    InputDeliveryMode.DOWNLOAD: InputOutputModes.DOWNLOAD,
+    InputDeliveryMode.DIRECT: InputOutputModes.DIRECT,
+    InputDeliveryMode.EVAL_MOUNT: InputOutputModes.EVAL_MOUNT,
+    InputDeliveryMode.EVAL_DOWNLOAD: InputOutputModes.EVAL_DOWNLOAD,
+}
+
+INPUT_MOUNT_MAPPING_TO_REST = {
+    InputOutputModes.MOUNT: InputDeliveryMode.READ_ONLY_MOUNT,
+    InputOutputModes.RW_MOUNT: InputDeliveryMode.READ_WRITE_MOUNT,
+    InputOutputModes.RO_MOUNT: InputDeliveryMode.READ_ONLY_MOUNT,
+    InputOutputModes.DOWNLOAD: InputDeliveryMode.DOWNLOAD,
+    InputOutputModes.EVAL_MOUNT: InputDeliveryMode.EVAL_MOUNT,
+    InputOutputModes.EVAL_DOWNLOAD: InputDeliveryMode.EVAL_DOWNLOAD,
+    InputOutputModes.DIRECT: InputDeliveryMode.DIRECT,
+}
+
+
+OUTPUT_MOUNT_MAPPING_FROM_REST = {
+    OutputDeliveryMode.READ_WRITE_MOUNT: InputOutputModes.RW_MOUNT,
+    OutputDeliveryMode.UPLOAD: InputOutputModes.UPLOAD,
+    OutputDeliveryMode.DIRECT: InputOutputModes.DIRECT,
+}
+
+OUTPUT_MOUNT_MAPPING_TO_REST = {
+    InputOutputModes.MOUNT: OutputDeliveryMode.READ_WRITE_MOUNT,
+    InputOutputModes.UPLOAD: OutputDeliveryMode.UPLOAD,
+    InputOutputModes.RW_MOUNT: OutputDeliveryMode.READ_WRITE_MOUNT,
+    InputOutputModes.DIRECT: OutputDeliveryMode.DIRECT,
+}
+
+
+# TODO: Remove this as both rest type and sdk type are snake case now.
+def get_output_type_mapping_from_rest() -> Dict[str, str]:
+    """Gets the mapping of JobOutputType to AssetType
+
+    :return: Mapping of JobOutputType to AssetType
+    :rtype: Dict[str, str]
+    """
+    return {
+        JobOutputType.URI_FILE: AssetTypes.URI_FILE,
+        JobOutputType.URI_FOLDER: AssetTypes.URI_FOLDER,
+        JobOutputType.MLTABLE: AssetTypes.MLTABLE,
+        JobOutputType.MLFLOW_MODEL: AssetTypes.MLFLOW_MODEL,
+        JobOutputType.CUSTOM_MODEL: AssetTypes.CUSTOM_MODEL,
+        JobOutputType.TRITON_MODEL: AssetTypes.TRITON_MODEL,
+    }
+
+
+def get_input_rest_cls_dict() -> Dict[str, RestJobInput]:
+    """Gets the mapping of AssetType to RestJobInput
+
+    :return: Map of AssetType to RestJobInput
+    :rtype: Dict[str, RestJobInput]
+    """
+    return {
+        AssetTypes.URI_FILE: RestUriFileJobInput,
+        AssetTypes.URI_FOLDER: RestUriFolderJobInput,
+        AssetTypes.MLTABLE: RestMLTableJobInput,
+        AssetTypes.MLFLOW_MODEL: RestMLFlowModelJobInput,
+        AssetTypes.CUSTOM_MODEL: RestCustomModelJobInput,
+        AssetTypes.TRITON_MODEL: RestTritonModelJobInput,
+    }
+
+
+def get_output_rest_cls_dict() -> Dict[str, RestJobOutput]:
+    """Get output rest init cls dict.
+
+    :return: Map of AssetType to RestJobOutput
+    :rtype: Dict[str, RestJobOutput]
+    """
+    return {
+        AssetTypes.URI_FILE: RestUriFileJobOutput,
+        AssetTypes.URI_FOLDER: RestUriFolderJobOutput,
+        AssetTypes.MLTABLE: RestMLTableJobOutput,
+        AssetTypes.MLFLOW_MODEL: RestMLFlowModelJobOutput,
+        AssetTypes.CUSTOM_MODEL: RestCustomModelJobOutput,
+        AssetTypes.TRITON_MODEL: RestTritonModelJobOutput,
+    }
+
+
+def build_input_output(
+    item: Union[InputOutputEntry, Input, Output, str, bool, int, float],
+    inputs: bool = True,
+) -> Union[InputOutputEntry, Input, Output, str, bool, int, float]:
+    if isinstance(item, (Input, InputOutputEntry, Output)):
+        # return objects constructed at yaml load or specified in sdk
+        return item
+    # parse dictionary into supported class
+    if isinstance(item, collections.abc.Mapping):
+        if item.get("data"):
+            return InputOutputEntry(**item)
+        # else default to JobInput
+        return Input(**item) if inputs else Output(**item)
+    # return literal inputs as-is
+    return item
+
+
+def _validate_inputs_for(input_consumer_name: str, input_consumer: str, inputs: Optional[Dict]) -> None:
+    implicit_inputs = re.findall(r"\${{inputs\.([\w\.-]+)}}", input_consumer)
+    # optional inputs no need to validate whether they're in inputs
+    optional_inputs = re.findall(r"\[[\w\.\s-]*\${{inputs\.([\w\.-]+)}}]", input_consumer)
+    for key in implicit_inputs:
+        if inputs is not None and inputs.get(key, None) is None and key not in optional_inputs:
+            msg = "Inputs to job does not contain '{}' referenced in " + input_consumer_name
+            raise ValidationException(
+                message=msg.format(key),
+                no_personal_data_message=msg.format("[key]"),
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.INVALID_VALUE,
+            )
+
+
+def validate_inputs_for_command(command: Optional[str], inputs: Optional[Dict]) -> None:
+    if command is not None:
+        _validate_inputs_for("command", command, inputs)
+
+
+def validate_inputs_for_args(args: str, inputs: Optional[Dict[str, Any]]) -> None:
+    _validate_inputs_for("args", args, inputs)
+
+
+def validate_key_contains_allowed_characters(key: str) -> None:
+    if re.match(r"^[a-zA-Z_]+[a-zA-Z0-9_]*$", key) is None:
+        msg = "Key name  {} must be composed letters, numbers, and underscore"
+        raise ValidationException(
+            message=msg.format(key),
+            no_personal_data_message=msg.format("[key]"),
+            target=ErrorTarget.JOB,
+            error_category=ErrorCategory.USER_ERROR,
+            error_type=ValidationErrorType.INVALID_VALUE,
+        )
+
+
+def validate_pipeline_input_key_characters(key: str) -> None:
+    # Pipeline input allow '.' to support parameter group in key.
+    # Note: ([a-zA-Z_]+[a-zA-Z0-9_]*) is a valid single key,
+    # so a valid pipeline key is: ^{single_key}([.]{single_key})*$
+    if re.match(IOConstants.VALID_KEY_PATTERN, key) is None:
+        msg = (
+            "Pipeline input key name {} must be composed letters, numbers, and underscores with optional split by dots."
+        )
+        raise ValidationException(
+            message=msg.format(key),
+            no_personal_data_message=msg.format("[key]"),
+            target=ErrorTarget.JOB,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+
+def to_rest_dataset_literal_inputs(
+    inputs: Optional[Dict],
+    *,
+    job_type: Optional[str],
+) -> Dict[str, RestJobInput]:
+    """Turns dataset and literal inputs into dictionary of REST JobInput.
+
+    :param inputs: Dictionary of dataset and literal inputs to job
+    :type inputs: Dict[str, Union[int, str, float, bool, JobInput]]
+    :return: A dictionary mapping input name to a ComponentJobInput or PipelineInput
+    :rtype: Dict[str, Union[ComponentJobInput, PipelineInput]]
+    :keyword job_type: When job_type is pipeline, enable dot('.') in parameter keys to support parameter group.
+        TODO: Remove this after move name validation to Job's customized validate.
+    :paramtype job_type: str
+    """
+    rest_inputs = {}
+
+    if inputs is not None:
+        # Pack up the inputs into REST format
+        for input_name, input_value in inputs.items():
+            if job_type == JobType.PIPELINE:
+                validate_pipeline_input_key_characters(input_name)
+            elif job_type:
+                # We pass job_type=None for pipeline node, and want skip this check for nodes.
+                validate_key_contains_allowed_characters(input_name)
+            if isinstance(input_value, Input):
+                if (
+                    input_value.path
+                    and isinstance(input_value.path, str)
+                    and is_data_binding_expression(input_value.path)
+                ):
+                    input_data = LiteralJobInput(value=input_value.path)
+                    # set mode attribute manually for binding job input
+                    if input_value.mode:
+                        input_data.mode = INPUT_MOUNT_MAPPING_TO_REST[input_value.mode]
+                    if getattr(input_value, "path_on_compute", None) is not None:
+                        input_data.pathOnCompute = input_value.path_on_compute
+                    input_data.job_input_type = JobInputType.LITERAL
+                else:
+                    target_cls_dict = get_input_rest_cls_dict()
+
+                    if input_value.type in target_cls_dict:
+                        input_data = target_cls_dict[input_value.type](
+                            uri=input_value.path,
+                            mode=(INPUT_MOUNT_MAPPING_TO_REST[input_value.mode.lower()] if input_value.mode else None),
+                        )
+                    else:
+                        msg = f"Job input type {input_value.type} is not supported as job input."
+                        raise ValidationException(
+                            message=msg,
+                            no_personal_data_message=msg,
+                            target=ErrorTarget.JOB,
+                            error_category=ErrorCategory.USER_ERROR,
+                            error_type=ValidationErrorType.INVALID_VALUE,
+                        )
+            elif input_value is None:
+                # If the input is None, we need to pass the origin None to the REST API
+                input_data = LiteralJobInput(value=None)
+            else:
+                # otherwise, the input is a literal input
+                if isinstance(input_value, dict):
+                    input_data = LiteralJobInput(value=str(input_value["value"]))
+                    # set mode attribute manually for binding job input
+                    if "mode" in input_value:
+                        input_data.mode = input_value["mode"]
+                else:
+                    input_data = LiteralJobInput(value=str(input_value))
+                input_data.job_input_type = JobInputType.LITERAL
+            # Pack up inputs into PipelineInputs or ComponentJobInputs depending on caller
+            rest_inputs[input_name] = input_data
+    return rest_inputs
+
+
+def from_rest_inputs_to_dataset_literal(inputs: Dict[str, RestJobInput]) -> Dict:
+    """Turns REST dataset and literal inputs into the SDK format.
+
+    :param inputs: Dictionary mapping input name to ComponentJobInput or PipelineInput
+    :type inputs: Dict[str, Union[ComponentJobInput, PipelineInput]]
+    :return: A dictionary mapping input name to a literal value or JobInput
+    :rtype: Dict[str, Union[int, str, float, bool, JobInput]]
+    """
+    if inputs is None:
+        return {}
+    from_rest_inputs = {}
+    # Unpack the inputs
+    for input_name, input_value in inputs.items():
+        # TODO:Brandon Clarify with PMs if user should be able to define null input objects
+        if input_value is None:
+            continue
+
+        # TODO: Remove this as both rest type and sdk type are snake case now.
+        type_transfer_dict = get_output_type_mapping_from_rest()
+        # deal with invalid input type submitted by feb api
+        # todo: backend help convert node level input/output type
+        normalize_job_input_output_type(input_value)
+
+        if input_value.job_input_type in type_transfer_dict:
+            if input_value.uri:
+                path = input_value.uri
+                if getattr(input_value, "pathOnCompute", None) is not None:
+                    sourcePathOnCompute = input_value.pathOnCompute
+                else:
+                    sourcePathOnCompute = None
+                input_data = Input(
+                    type=type_transfer_dict[input_value.job_input_type],
+                    path=path,
+                    mode=(INPUT_MOUNT_MAPPING_FROM_REST[input_value.mode] if input_value.mode else None),
+                    path_on_compute=sourcePathOnCompute,
+                )
+        elif input_value.job_input_type in (JobInputType.LITERAL, JobInputType.LITERAL):
+            # otherwise, the input is a literal, so just unpack the InputData value field
+            input_data = input_value.value
+        else:
+            msg = f"Job input type {input_value.job_input_type} is not supported as job input."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.INVALID_VALUE,
+            )
+
+        from_rest_inputs[input_name] = input_data  # pylint: disable=possibly-used-before-assignment
+    return from_rest_inputs
+
+
+def to_rest_data_outputs(outputs: Optional[Dict]) -> Dict[str, RestJobOutput]:
+    """Turns job outputs into REST format.
+
+    :param outputs: Dictionary of dataset outputs from job
+    :type outputs: Dict[str, JobOutput]
+    :return: A dictionary mapping output name to a RestJobOutput
+    :rtype: Dict[str, RestJobOutput]
+    """
+    rest_outputs = {}
+    if outputs is not None:
+        for output_name, output_value in outputs.items():
+            validate_key_contains_allowed_characters(output_name)
+            if output_value is None:
+                # pipeline output could be none, default to URI folder with None mode
+                output_cls = RestUriFolderJobOutput
+                rest_outputs[output_name] = output_cls(mode=None)
+            else:
+                target_cls_dict = get_output_rest_cls_dict()
+
+                output_value_type = output_value.type if output_value.type else AssetTypes.URI_FOLDER
+                if output_value_type in target_cls_dict:
+                    output = target_cls_dict[output_value_type](
+                        asset_name=output_value.name,
+                        asset_version=output_value.version,
+                        uri=output_value.path,
+                        mode=(OUTPUT_MOUNT_MAPPING_TO_REST[output_value.mode.lower()] if output_value.mode else None),
+                        pathOnCompute=getattr(output_value, "path_on_compute", None),
+                        description=output_value.description,
+                    )
+                else:
+                    msg = "unsupported JobOutput type: {}".format(output_value.type)
+                    raise ValidationException(
+                        message=msg,
+                        no_personal_data_message=msg,
+                        target=ErrorTarget.JOB,
+                        error_category=ErrorCategory.USER_ERROR,
+                        error_type=ValidationErrorType.INVALID_VALUE,
+                    )
+                rest_outputs[output_name] = output
+    return rest_outputs
+
+
+def from_rest_data_outputs(outputs: Dict[str, RestJobOutput]) -> Dict[str, Output]:
+    """Turns REST outputs into the SDK format.
+
+    :param outputs: Dictionary of dataset and literal inputs to job
+    :type outputs: Dict[str, RestJobOutput]
+    :return: A dictionary mapping input name to a InputOutputEntry
+    :rtype: Dict[str, JobOutput]
+    """
+    output_type_mapping = get_output_type_mapping_from_rest()
+    from_rest_outputs = {}
+    if outputs is None:
+        return {}
+    for output_name, output_value in outputs.items():
+        if output_value is None:
+            continue
+        # deal with invalid output type submitted by feb api
+        # todo: backend help convert node level input/output type
+        normalize_job_input_output_type(output_value)
+        if getattr(output_value, "pathOnCompute", None) is not None:
+            sourcePathOnCompute = output_value.pathOnCompute
+        else:
+            sourcePathOnCompute = None
+        if output_value.job_output_type in output_type_mapping:
+            from_rest_outputs[output_name] = Output(
+                type=output_type_mapping[output_value.job_output_type],
+                path=output_value.uri,
+                mode=(OUTPUT_MOUNT_MAPPING_FROM_REST[output_value.mode] if output_value.mode else None),
+                path_on_compute=sourcePathOnCompute,
+                description=output_value.description,
+                name=output_value.asset_name,
+                version=(output_value.asset_version if hasattr(output_value, "asset_version") else None),
+            )
+        else:
+            msg = "unsupported JobOutput type: {}".format(output_value.job_output_type)
+            raise JobException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.SYSTEM_ERROR,
+            )
+
+    return from_rest_outputs
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/_studio_url_from_job_id.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/_studio_url_from_job_id.py
new file mode 100644
index 00000000..63ad6f06
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/_studio_url_from_job_id.py
@@ -0,0 +1,26 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import re
+from typing import Optional
+
+from azure.ai.ml._azure_environments import _get_aml_resource_id_from_metadata, _get_default_cloud_name
+
+JOB_ID_RE_PATTERN = re.compile(
+    (
+        r"\/subscriptions\/(?P<subscription>[\w,-]+)\/resourceGroups\/(?P<resource_group>[\w,-]+)\/providers"
+        r"\/Microsoft\.MachineLearningServices\/workspaces\/(?P<workspace>[\w,-]+)\/jobs\/(?P<run_id>[\w,-]+)"
+    )  # fmt: skip
+)
+
+
+def studio_url_from_job_id(job_id: str) -> Optional[str]:
+    resource_id = _get_aml_resource_id_from_metadata(_get_default_cloud_name())
+    m = JOB_ID_RE_PATTERN.match(job_id)
+    if m:
+        return (
+            f"{resource_id}/runs/{m.group('run_id')}?wsid=/subscriptions/{m.group('subscription')}"
+            f"/resourcegroups/{m.group('resource_group')}/workspaces/{m.group('workspace')}"
+        )  # fmt: skip
+    return None
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/__init__.py
new file mode 100644
index 00000000..e99e9321
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/__init__.py
@@ -0,0 +1,16 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
+
+from .search_space import SearchSpace
+from .stack_ensemble_settings import StackEnsembleSettings
+from .training_settings import ClassificationTrainingSettings, TrainingSettings
+
+__all__ = [
+    "ClassificationTrainingSettings",
+    "TrainingSettings",
+    "SearchSpace",
+    "StackEnsembleSettings",
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/automl_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/automl_job.py
new file mode 100644
index 00000000..9e1b4d05
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/automl_job.py
@@ -0,0 +1,283 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import logging
+from abc import ABC, abstractmethod
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2024_01_01_preview.models import (
+    JobBase,
+    MLTableJobInput,
+    QueueSettings,
+    ResourceConfiguration,
+    TaskType,
+)
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.constants._common import TYPE, AssetTypes
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import (
+    AmlTokenConfiguration,
+    ManagedIdentityConfiguration,
+    UserIdentityConfiguration,
+)
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job.job import Job
+from azure.ai.ml.entities._job.job_io_mixin import JobIOMixin
+from azure.ai.ml.entities._job.pipeline._io import AutoMLNodeIOMixin
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+module_logger = logging.getLogger(__name__)
+
+
+class AutoMLJob(Job, JobIOMixin, AutoMLNodeIOMixin, ABC):
+    """Initialize an AutoML job entity.
+
+    Constructor for an AutoMLJob.
+
+    :keyword resources: Resource configuration for the AutoML job, defaults to None
+    :paramtype resources: typing.Optional[ResourceConfiguration]
+    :keyword identity: Identity that training job will use while running on compute, defaults to None
+    :paramtype identity: typing.Optional[ typing.Union[ManagedIdentityConfiguration, AmlTokenConfiguration
+        , UserIdentityConfiguration] ]
+    :keyword environment_id: The environment id for the AutoML job, defaults to None
+    :paramtype environment_id: typing.Optional[str]
+    :keyword environment_variables: The environment variables for the AutoML job, defaults to None
+    :paramtype environment_variables: typing.Optional[Dict[str, str]]
+    :keyword outputs: The outputs for the AutoML job, defaults to None
+    :paramtype outputs: typing.Optional[Dict[str, str]]
+    :keyword queue_settings: The queue settings for the AutoML job, defaults to None
+    :paramtype queue_settings: typing.Optional[QueueSettings]
+    :raises ValidationException: task type validation error
+    :raises NotImplementedError: Raises NotImplementedError
+    :return: An AutoML Job
+    :rtype: AutoMLJob
+    """
+
+    def __init__(
+        self,
+        *,
+        resources: Optional[ResourceConfiguration] = None,
+        identity: Optional[
+            Union[ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]
+        ] = None,
+        queue_settings: Optional[QueueSettings] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize an AutoML job entity.
+
+        Constructor for an AutoMLJob.
+
+        :keyword resources: Resource configuration for the AutoML job, defaults to None
+        :paramtype resources: typing.Optional[ResourceConfiguration]
+        :keyword identity: Identity that training job will use while running on compute, defaults to None
+        :paramtype identity: typing.Optional[ typing.Union[ManagedIdentityConfiguration, AmlTokenConfiguration
+            , UserIdentityConfiguration] ]
+        :keyword environment_id: The environment id for the AutoML job, defaults to None
+        :paramtype environment_id: typing.Optional[str]
+        :keyword environment_variables: The environment variables for the AutoML job, defaults to None
+        :paramtype environment_variables: typing.Optional[Dict[str, str]]
+        :keyword outputs: The outputs for the AutoML job, defaults to None
+        :paramtype outputs: typing.Optional[Dict[str, str]]
+        :keyword queue_settings: The queue settings for the AutoML job, defaults to None
+        :paramtype queue_settings: typing.Optional[QueueSettings]
+        :raises ValidationException: task type validation error
+        :raises NotImplementedError: Raises NotImplementedError
+        """
+        kwargs[TYPE] = JobType.AUTOML
+        self.environment_id = kwargs.pop("environment_id", None)
+        self.environment_variables = kwargs.pop("environment_variables", None)
+        self.outputs = kwargs.pop("outputs", None)
+
+        super().__init__(**kwargs)
+
+        self.resources = resources
+        self.identity = identity
+        self.queue_settings = queue_settings
+
+    @property
+    @abstractmethod
+    def training_data(self) -> Input:
+        """The training data for the AutoML job.
+
+        :raises NotImplementedError: Raises NotImplementedError
+        :return: Returns the training data for the AutoML job.
+        :rtype: Input
+        """
+        raise NotImplementedError()
+
+    @training_data.setter
+    def training_data(self, value: Any) -> None:
+        self.training_data = value
+
+    @property
+    @abstractmethod
+    def validation_data(self) -> Input:
+        """The validation data for the AutoML job.
+
+        :raises NotImplementedError: Raises NotImplementedError
+        :return: Returns the validation data for the AutoML job.
+        :rtype: Input
+        """
+        raise NotImplementedError()
+
+    @validation_data.setter
+    def validation_data(self, value: Any) -> None:
+        self.validation_data = value
+
+    @property
+    @abstractmethod
+    def test_data(self) -> Input:
+        """The test data for the AutoML job.
+
+        :raises NotImplementedError: Raises NotImplementedError
+        :return: Returns the test data for the AutoML job.
+        :rtype: Input
+        """
+        raise NotImplementedError()
+
+    @test_data.setter
+    def test_data(self, value: Any) -> None:
+        self.test_data = value
+
+    @classmethod
+    def _load_from_rest(cls, obj: JobBase) -> "AutoMLJob":
+        """Loads the rest object to a dict containing items to init the AutoMLJob objects.
+
+        :param obj: Azure Resource Manager resource envelope.
+        :type obj: JobBase
+        :raises ValidationException: task type validation error
+        :return: An AutoML Job
+        :rtype: AutoMLJob
+        """
+        task_type = (
+            camel_to_snake(obj.properties.task_details.task_type) if obj.properties.task_details.task_type else None
+        )
+        class_type = cls._get_task_mapping().get(task_type, None)
+        if class_type:
+            res: AutoMLJob = class_type._from_rest_object(obj)
+            return res
+        msg = f"Unsupported task type: {obj.properties.task_details.task_type}"
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.AUTOML,
+            error_category=ErrorCategory.SYSTEM_ERROR,
+        )
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "AutoMLJob":
+        """Loads the dictionary objects to an AutoMLJob object.
+
+        :param data: A data dictionary.
+        :type data: typing.Dict
+        :param context: A context dictionary.
+        :type context: typing.Dict
+        :param additional_message: An additional message to be logged in the ValidationException.
+        :type additional_message: str
+
+        :raises ValidationException: task type validation error
+        :return: An AutoML Job
+        :rtype: AutoMLJob
+        """
+        task_type = data.get(AutoMLConstants.TASK_TYPE_YAML)
+        class_type = cls._get_task_mapping().get(task_type, None)
+        if class_type:
+            res: AutoMLJob = class_type._load_from_dict(
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+            return res
+        msg = f"Unsupported task type: {task_type}"
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.AUTOML,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "AutoMLJob":
+        """Create an automl job instance from schema parsed dict.
+
+        :param loaded_data:  A loaded_data dictionary.
+        :type loaded_data: typing.Dict
+        :raises ValidationException: task type validation error
+        :return: An AutoML Job
+        :rtype: AutoMLJob
+        """
+        task_type = loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML)
+        class_type = cls._get_task_mapping().get(task_type, None)
+        if class_type:
+            res: AutoMLJob = class_type._create_instance_from_schema_dict(loaded_data=loaded_data)
+            return res
+        msg = f"Unsupported task type: {task_type}"
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.AUTOML,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+    @classmethod
+    def _get_task_mapping(cls) -> Dict:
+        """Create a mapping of task type to job class.
+
+        :return: An AutoMLVertical object containing the task type to job class mapping.
+        :rtype: AutoMLVertical
+        """
+        from .image import (
+            ImageClassificationJob,
+            ImageClassificationMultilabelJob,
+            ImageInstanceSegmentationJob,
+            ImageObjectDetectionJob,
+        )
+        from .nlp import TextClassificationJob, TextClassificationMultilabelJob, TextNerJob
+        from .tabular import ClassificationJob, ForecastingJob, RegressionJob
+
+        # create a mapping of task type to job class
+        return {
+            camel_to_snake(TaskType.CLASSIFICATION): ClassificationJob,
+            camel_to_snake(TaskType.REGRESSION): RegressionJob,
+            camel_to_snake(TaskType.FORECASTING): ForecastingJob,
+            camel_to_snake(TaskType.IMAGE_CLASSIFICATION): ImageClassificationJob,
+            camel_to_snake(TaskType.IMAGE_CLASSIFICATION_MULTILABEL): ImageClassificationMultilabelJob,
+            camel_to_snake(TaskType.IMAGE_OBJECT_DETECTION): ImageObjectDetectionJob,
+            camel_to_snake(TaskType.IMAGE_INSTANCE_SEGMENTATION): ImageInstanceSegmentationJob,
+            camel_to_snake(TaskType.TEXT_NER): TextNerJob,
+            camel_to_snake(TaskType.TEXT_CLASSIFICATION): TextClassificationJob,
+            camel_to_snake(TaskType.TEXT_CLASSIFICATION_MULTILABEL): TextClassificationMultilabelJob,
+        }
+
+    def _resolve_data_inputs(self, rest_job: "AutoMLJob") -> None:
+        """Resolve JobInputs to MLTableJobInputs within data_settings.
+
+        :param rest_job: The rest job object.
+        :type rest_job: AutoMLJob
+        """
+        if isinstance(rest_job.training_data, Input):
+            rest_job.training_data = MLTableJobInput(uri=rest_job.training_data.path)
+        if isinstance(rest_job.validation_data, Input):
+            rest_job.validation_data = MLTableJobInput(uri=rest_job.validation_data.path)
+        if hasattr(rest_job, "test_data") and isinstance(rest_job.test_data, Input):
+            rest_job.test_data = MLTableJobInput(uri=rest_job.test_data.path)
+
+    def _restore_data_inputs(self) -> None:
+        """Restore MLTableJobInputs to JobInputs within data_settings."""
+        if isinstance(self.training_data, MLTableJobInput):
+            self.training_data = Input(type=AssetTypes.MLTABLE, path=self.training_data.uri)
+        if isinstance(self.validation_data, MLTableJobInput):
+            self.validation_data = Input(type=AssetTypes.MLTABLE, path=self.validation_data.uri)
+        if hasattr(self, "test_data") and isinstance(self.test_data, MLTableJobInput):
+            self.test_data = Input(type=AssetTypes.MLTABLE, path=self.test_data.uri)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/automl_vertical.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/automl_vertical.py
new file mode 100644
index 00000000..f11be81c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/automl_vertical.py
@@ -0,0 +1,134 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from abc import abstractmethod
+from typing import Any, Optional
+
+from azure.ai.ml import Input
+
+from .automl_job import AutoMLJob
+
+
+class AutoMLVertical(AutoMLJob):
+    """Abstract class for AutoML verticals.
+
+    :param task_type: The type of task to run. Possible values include: "classification", "regression", "forecasting".
+    :type task_type: str
+    :param training_data: Training data input
+    :type training_data: Input
+    :param validation_data: Validation data input
+    :type validation_data: Input
+    :param test_data: Test data input, defaults to None
+    :type test_data: typing.Optional[Input]
+    :raises ValueError: If task_type is not one of "classification", "regression", "forecasting".
+    :raises ValueError: If training_data is not of type Input.
+    :raises ValueError: If validation_data is not of type Input.
+    :raises ValueError: If test_data is not of type Input.
+    """
+
+    @abstractmethod
+    def __init__(
+        self,
+        task_type: str,
+        training_data: Input,
+        validation_data: Input,
+        test_data: Optional[Input] = None,
+        **kwargs: Any
+    ) -> None:
+        """Initialize AutoMLVertical.
+
+        Constructor for AutoMLVertical.
+
+        :param task_type: The type of task to run. Possible values include: "classification", "regression"
+            , "forecasting".
+        :type task_type: str
+        :param training_data: Training data input
+        :type training_data: Input
+        :param validation_data: Validation data input
+        :type validation_data: Input
+        :param test_data: Test data input, defaults to None
+        :type test_data: typing.Optional[Input]
+        :raises ValueError: If task_type is not one of "classification", "regression", "forecasting".
+        :raises ValueError: If training_data is not of type Input.
+        :raises ValueError: If validation_data is not of type Input.
+        :raises ValueError: If test_data is not of type Input.
+        """
+        self._task_type = task_type
+        self.training_data = training_data
+        self.validation_data = validation_data
+        self.test_data = test_data  # type: ignore
+        super().__init__(**kwargs)
+
+    @property
+    def task_type(self) -> str:
+        """Get task type.
+
+        :return: The type of task to run. Possible values include: "classification", "regression", "forecasting".
+        :rtype: str
+        """
+        return self._task_type
+
+    @task_type.setter
+    def task_type(self, task_type: str) -> None:
+        """Set task type.
+
+        :param task_type: The type of task to run. Possible values include: "classification", "regression"
+            , "forecasting".
+        :type task_type: str
+        """
+        self._task_type = task_type
+
+    @property
+    def training_data(self) -> Input:
+        """Get training data.
+
+        :return: Training data input
+        :rtype: Input
+        """
+        return self._training_data
+
+    @training_data.setter
+    def training_data(self, training_data: Input) -> None:
+        """Set training data.
+
+        :param training_data: Training data input
+        :type training_data: Input
+        """
+        self._training_data = training_data
+
+    @property
+    def validation_data(self) -> Input:
+        """Get validation data.
+
+        :return: Validation data input
+        :rtype: Input
+        """
+        return self._validation_data
+
+    @validation_data.setter
+    def validation_data(self, validation_data: Input) -> None:
+        """Set validation data.
+
+        :param validation_data: Validation data input
+        :type validation_data: Input
+        """
+        self._validation_data = validation_data
+
+    @property
+    def test_data(self) -> Input:
+        """Get test data.
+
+        :return: Test data input
+        :rtype: Input
+        """
+        return self._test_data
+
+    @test_data.setter
+    def test_data(self, test_data: Input) -> None:
+        """Set test data.
+
+        :param test_data: Test data input
+        :type test_data: Input
+        """
+        self._test_data = test_data
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/featurization_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/featurization_settings.py
new file mode 100644
index 00000000..c9e73d21
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/featurization_settings.py
@@ -0,0 +1,32 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional
+
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class FeaturizationSettings(RestTranslatableMixin):
+    """Base Featurization settings."""
+
+    def __init__(
+        self,
+        *,
+        dataset_language: Optional[str] = None,
+    ):
+        self.dataset_language = dataset_language
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, FeaturizationSettings):
+            return NotImplemented
+
+        return self.dataset_language == other.dataset_language
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
+
+
+class FeaturizationSettingsType:
+    NLP = "nlp"
+    TABULAR = "tabular"
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/__init__.py
new file mode 100644
index 00000000..46964086
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/__init__.py
@@ -0,0 +1,35 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from .automl_image import AutoMLImage
+from .image_classification_job import ImageClassificationJob
+from .image_classification_multilabel_job import ImageClassificationMultilabelJob
+from .image_classification_search_space import ImageClassificationSearchSpace
+from .image_instance_segmentation_job import ImageInstanceSegmentationJob
+from .image_limit_settings import ImageLimitSettings
+from .image_model_settings import (
+    ImageModelSettingsClassification,
+    ImageModelSettingsObjectDetection,
+    LogTrainingMetrics,
+    LogValidationLoss,
+)
+from .image_object_detection_job import ImageObjectDetectionJob
+from .image_object_detection_search_space import ImageObjectDetectionSearchSpace
+from .image_sweep_settings import ImageSweepSettings
+
+__all__ = [
+    "AutoMLImage",
+    "LogTrainingMetrics",
+    "LogValidationLoss",
+    "ImageClassificationJob",
+    "ImageClassificationMultilabelJob",
+    "ImageClassificationSearchSpace",
+    "ImageInstanceSegmentationJob",
+    "ImageLimitSettings",
+    "ImageObjectDetectionJob",
+    "ImageObjectDetectionSearchSpace",
+    "ImageSweepSettings",
+    "ImageModelSettingsClassification",
+    "ImageModelSettingsObjectDetection",
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/automl_image.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/automl_image.py
new file mode 100644
index 00000000..a07bba4a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/automl_image.py
@@ -0,0 +1,244 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from abc import ABC
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import LogVerbosity, SamplingAlgorithmType
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job.automl.automl_vertical import AutoMLVertical
+from azure.ai.ml.entities._job.automl.image.image_limit_settings import ImageLimitSettings
+from azure.ai.ml.entities._job.automl.image.image_sweep_settings import ImageSweepSettings
+from azure.ai.ml.entities._job.sweep.early_termination_policy import (
+    BanditPolicy,
+    MedianStoppingPolicy,
+    TruncationSelectionPolicy,
+)
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+class AutoMLImage(AutoMLVertical, ABC):
+    """Base class for all AutoML Image jobs.
+    You should not instantiate this class directly.
+    Instead you should create classes for specific AutoML Image tasks.
+
+    :keyword task_type: Required. Type of task to run.
+    Possible values include: "ImageClassification", "ImageClassificationMultilabel",
+                              "ImageObjectDetection", "ImageInstanceSegmentation"
+    :paramtype task_type: str
+    :keyword limits: Limit settings for all AutoML Image jobs. Defaults to None.
+    :paramtype limits: Optional[~azure.ai.ml.automl.ImageLimitSettings]
+    :keyword sweep: Sweep settings for all AutoML Image jobs. Defaults to None.
+    :paramtype sweep: Optional[~azure.ai.ml.automl.ImageSweepSettings]
+    :keyword kwargs: Additional keyword arguments for AutoMLImage.
+    :paramtype kwargs: Dict[str, Any]
+    """
+
+    def __init__(
+        self,
+        *,
+        task_type: str,
+        limits: Optional[ImageLimitSettings] = None,
+        sweep: Optional[ImageSweepSettings] = None,
+        **kwargs: Any,
+    ) -> None:
+        self.log_verbosity = kwargs.pop("log_verbosity", LogVerbosity.INFO)
+        self.target_column_name = kwargs.pop("target_column_name", None)
+        self.validation_data_size = kwargs.pop("validation_data_size", None)
+
+        super().__init__(
+            task_type=task_type,
+            training_data=kwargs.pop("training_data", None),
+            validation_data=kwargs.pop("validation_data", None),
+            **kwargs,
+        )
+
+        # Set default value for self._limits as it is a required property in rest object.
+        self._limits = limits or ImageLimitSettings()
+        self._sweep = sweep
+
+    @property
+    def log_verbosity(self) -> LogVerbosity:
+        """Returns the verbosity of the logger.
+
+        :return: The log verbosity.
+        :rtype: ~azure.ai.ml._restclient.v2023_04_01_preview.models.LogVerbosity
+        """
+        return self._log_verbosity
+
+    @log_verbosity.setter
+    def log_verbosity(self, value: Union[str, LogVerbosity]) -> None:
+        """Sets the verbosity of the logger.
+
+        :param value: The value to set the log verbosity to.
+                      Possible values include: "NotSet", "Debug", "Info", "Warning", "Error", "Critical".
+        :type value: Union[str, ~azure.ai.ml._restclient.v2023_04_01_preview.models.LogVerbosity]
+        """
+        self._log_verbosity = None if value is None else LogVerbosity[camel_to_snake(value).upper()]
+
+    @property
+    def limits(self) -> ImageLimitSettings:
+        """Returns the limit settings for all AutoML Image jobs.
+
+        :return: The limit settings.
+        :rtype: ~azure.ai.ml.automl.ImageLimitSettings
+        """
+        return self._limits
+
+    @limits.setter
+    def limits(self, value: Union[Dict, ImageLimitSettings]) -> None:
+        if isinstance(value, ImageLimitSettings):
+            self._limits = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for limit settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_limits(**value)
+
+    @property
+    def sweep(self) -> Optional[ImageSweepSettings]:
+        """Returns the sweep settings for all AutoML Image jobs.
+
+        :return: The sweep settings.
+        :rtype: ~azure.ai.ml.automl.ImageSweepSettings
+        """
+        return self._sweep
+
+    @sweep.setter
+    def sweep(self, value: Union[Dict, ImageSweepSettings]) -> None:
+        """Sets the sweep settings for all AutoML Image jobs.
+
+        :param value: The value to set the sweep settings to.
+        :type value: Union[Dict, ~azure.ai.ml.automl.ImageSweepSettings]
+        :raises ~azure.ai.ml.exceptions.ValidationException: If value is not a dictionary.
+        :return: None
+        """
+        if isinstance(value, ImageSweepSettings):
+            self._sweep = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for sweep settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_sweep(**value)
+
+    def set_data(
+        self,
+        *,
+        training_data: Input,
+        target_column_name: str,
+        validation_data: Optional[Input] = None,
+        validation_data_size: Optional[float] = None,
+    ) -> None:
+        """Data settings for all AutoML Image jobs.
+
+        :keyword training_data: Required. Training data.
+        :type training_data: ~azure.ai.ml.entities.Input
+        :keyword target_column_name: Required. Target column name.
+        :type target_column_name: str
+        :keyword validation_data: Optional. Validation data.
+        :type validation_data: Optional[~azure.ai.ml.entities.Input]
+        :keyword validation_data_size: Optional. The fraction of training dataset that needs to be set aside for
+                                      validation purpose. Values should be in range (0.0 , 1.0).
+                                      Applied only when validation dataset is not provided.
+        :type validation_data_size: Optional[float]
+        :return: None
+        """
+        self.target_column_name = self.target_column_name if target_column_name is None else target_column_name
+        self.training_data = self.training_data if training_data is None else training_data
+        self.validation_data = self.validation_data if validation_data is None else validation_data
+        self.validation_data_size = self.validation_data_size if validation_data_size is None else validation_data_size
+
+    def set_limits(
+        self,
+        *,
+        max_concurrent_trials: Optional[int] = None,
+        max_trials: Optional[int] = None,
+        timeout_minutes: Optional[int] = None,
+    ) -> None:
+        """Limit settings for all AutoML Image Jobs.
+
+        :keyword max_concurrent_trials: Maximum number of trials to run concurrently.
+        :type max_concurrent_trials: Optional[int]. Defaults to None.
+        :keyword max_trials: Maximum number of trials to run. Defaults to None.
+        :type max_trials: Optional[int]
+        :keyword timeout_minutes: AutoML job timeout.
+        :type timeout_minutes: ~datetime.timedelta
+        :return: None
+        """
+        self._limits = self._limits or ImageLimitSettings()
+        self._limits.max_concurrent_trials = (
+            max_concurrent_trials if max_concurrent_trials is not None else self._limits.max_concurrent_trials
+        )
+        self._limits.max_trials = max_trials if max_trials is not None else self._limits.max_trials
+        self._limits.timeout_minutes = timeout_minutes if timeout_minutes is not None else self._limits.timeout_minutes
+
+    def set_sweep(
+        self,
+        *,
+        sampling_algorithm: Union[
+            str, SamplingAlgorithmType.RANDOM, SamplingAlgorithmType.GRID, SamplingAlgorithmType.BAYESIAN
+        ],
+        early_termination: Optional[Union[BanditPolicy, MedianStoppingPolicy, TruncationSelectionPolicy]] = None,
+    ) -> None:
+        """Sweep settings for all AutoML Image jobs.
+
+        :keyword sampling_algorithm: Required. Type of the hyperparameter sampling
+            algorithms. Possible values include: "Grid", "Random", "Bayesian".
+        :type sampling_algorithm: Union[str, ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType.RANDOM,
+            ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType.GRID,
+            ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType.BAYESIAN]
+        :keyword early_termination: Type of early termination policy.
+        :type early_termination: Union[
+            ~azure.mgmt.machinelearningservices.models.BanditPolicy,
+            ~azure.mgmt.machinelearningservices.models.MedianStoppingPolicy,
+            ~azure.mgmt.machinelearningservices.models.TruncationSelectionPolicy]
+        :return: None
+        """
+        if self._sweep:
+            self._sweep.sampling_algorithm = sampling_algorithm
+        else:
+            self._sweep = ImageSweepSettings(sampling_algorithm=sampling_algorithm)
+
+        self._sweep.early_termination = early_termination or self._sweep.early_termination
+
+    def __eq__(self, other: object) -> bool:
+        """Compares two AutoMLImage objects for equality.
+
+        :param other: The other AutoMLImage object to compare to.
+        :type other: ~azure.ai.ml.automl.AutoMLImage
+        :return: True if the two AutoMLImage objects are equal; False otherwise.
+        :rtype: bool
+        """
+        if not isinstance(other, AutoMLImage):
+            return NotImplemented
+
+        return (
+            self.target_column_name == other.target_column_name
+            and self.training_data == other.training_data
+            and self.validation_data == other.validation_data
+            and self.validation_data_size == other.validation_data_size
+            and self._limits == other._limits
+            and self._sweep == other._sweep
+        )
+
+    def __ne__(self, other: object) -> bool:
+        """Compares two AutoMLImage objects for inequality.
+
+        :param other: The other AutoMLImage object to compare to.
+        :type other: ~azure.ai.ml.automl.AutoMLImage
+        :return: True if the two AutoMLImage objects are not equal; False otherwise.
+        :rtype: bool
+        """
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/automl_image_classification_base.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/automl_image_classification_base.py
new file mode 100644
index 00000000..ef0c8a2d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/automl_image_classification_base.py
@@ -0,0 +1,439 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict, List, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import LearningRateScheduler, StochasticOptimizer
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.entities._job.automl.image.automl_image import AutoMLImage
+from azure.ai.ml.entities._job.automl.image.image_classification_search_space import ImageClassificationSearchSpace
+from azure.ai.ml.entities._job.automl.image.image_limit_settings import ImageLimitSettings
+from azure.ai.ml.entities._job.automl.image.image_model_settings import ImageModelSettingsClassification
+from azure.ai.ml.entities._job.automl.image.image_sweep_settings import ImageSweepSettings
+from azure.ai.ml.entities._job.automl.search_space import SearchSpace
+from azure.ai.ml.entities._job.automl.utils import cast_to_specific_search_space
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+class AutoMLImageClassificationBase(AutoMLImage):
+    """Base class for AutoML Image Classification and Image Classification Multilabel tasks.
+    Please do not instantiate this class directly. Instantiate one of the child classes instead.
+
+    :keyword task_type: Type of task to run.
+    Possible values include: "ImageClassification", "ImageClassificationMultilabel".
+    :paramtype task_type: str
+    :keyword limits: Limits for Automl image classification jobs. Defaults to None.
+    :paramtype limits: Optional[~azure.ai.ml.automl.ImageLimitSettings]
+    :keyword sweep: Sweep settings for Automl image classification jobs. Defaults to None.
+    :paramtype sweep: Optional[~azure.ai.ml.automl.ImageSweepSettings]
+    :keyword training_parameters: Training parameters for Automl image classification jobs. Defaults to None.
+    :paramtype training_parameters: Optional[~azure.ai.ml.automl.ImageModelSettingsClassification]
+    :keyword search_space: Search space for Automl image classification jobs. Defaults to None.
+    :paramtype search_space: Optional[List[~azure.ai.ml.automl.ImageClassificationSearchSpace]]
+    :keyword kwargs: Other Keyword arguments for AutoMLImageClassificationBase class.
+    :paramtype kwargs: Dict[str, Any]
+    """
+
+    def __init__(
+        self,
+        *,
+        task_type: str,
+        limits: Optional[ImageLimitSettings] = None,
+        sweep: Optional[ImageSweepSettings] = None,
+        training_parameters: Optional[ImageModelSettingsClassification] = None,
+        search_space: Optional[List[ImageClassificationSearchSpace]] = None,
+        **kwargs: Any,
+    ) -> None:
+        self._training_parameters: Optional[ImageModelSettingsClassification] = None
+
+        super().__init__(task_type=task_type, limits=limits, sweep=sweep, **kwargs)
+        self.training_parameters = training_parameters  # Assigning training_parameters through setter method.
+        self._search_space = search_space
+
+    @property
+    def training_parameters(self) -> Optional[ImageModelSettingsClassification]:
+        """
+        :rtype: ~azure.ai.ml.automl.ImageModelSettingsClassification
+        :return: Training parameters for AutoML Image Classification and Image Classification Multilabel tasks.
+        """
+        return self._training_parameters
+
+    @training_parameters.setter
+    def training_parameters(self, value: Union[Dict, ImageModelSettingsClassification]) -> None:
+        """Setting Image training parameters for AutoML Image Classification and Image Classification Multilabel tasks.
+
+        :param value: Training parameters for AutoML Image Classification and Image Classification Multilabel tasks.
+        :type value: Union[Dict, ~azure.ai.ml.automl.ImageModelSettingsClassification]
+        :raises ~azure.ml.exceptions.ValidationException if value is not a dictionary or
+         ImageModelSettingsClassification.
+        :return: None
+        """
+        if value is None:
+            self._training_parameters = None
+        elif isinstance(value, ImageModelSettingsClassification):
+            self._training_parameters = value
+            # set_training_parameters convert parameter values from snake case str to enum.
+            # We need to add any future enum parameters in this call to support snake case str.
+            self.set_training_parameters(
+                optimizer=value.optimizer,
+                learning_rate_scheduler=value.learning_rate_scheduler,
+            )
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for model settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_training_parameters(**value)
+
+    @property
+    def search_space(self) -> Optional[List[ImageClassificationSearchSpace]]:
+        """
+        :rtype: List[~azure.ai.ml.automl.ImageClassificationSearchSpace]
+        :return: Search space for AutoML Image Classification and Image Classification Multilabel tasks.
+        """
+        return self._search_space
+
+    @search_space.setter
+    def search_space(self, value: Union[List[Dict], List[SearchSpace]]) -> None:
+        """Setting Image search space for AutoML Image Classification and Image Classification Multilabel tasks.
+
+        :param value: Search space for AutoML Image Classification and Image Classification Multilabel tasks.
+        :type value: Union[List[Dict], List[~azure.ai.ml.automl.ImageClassificationSearchSpace]]
+        :raises ~azure.ml.exceptions.ValidationException if value is not a list of dictionaries or
+            ImageClassificationSearchSpace.
+        """
+        if not isinstance(value, list):
+            msg = "Expected a list for search space."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+        all_dict_type = all(isinstance(item, dict) for item in value)
+        all_search_space_type = all(isinstance(item, SearchSpace) for item in value)
+
+        if all_search_space_type or all_dict_type:
+            self._search_space = [
+                cast_to_specific_search_space(item, ImageClassificationSearchSpace, self.task_type)  # type: ignore
+                for item in value
+            ]
+        else:
+            msg = "Expected all items in the list to be either dictionaries or ImageClassificationSearchSpace objects."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    # pylint: disable=too-many-locals
+    def set_training_parameters(
+        self,
+        *,
+        advanced_settings: Optional[str] = None,
+        ams_gradient: Optional[bool] = None,
+        beta1: Optional[float] = None,
+        beta2: Optional[float] = None,
+        checkpoint_frequency: Optional[int] = None,
+        checkpoint_run_id: Optional[str] = None,
+        distributed: Optional[bool] = None,
+        early_stopping: Optional[bool] = None,
+        early_stopping_delay: Optional[int] = None,
+        early_stopping_patience: Optional[int] = None,
+        enable_onnx_normalization: Optional[bool] = None,
+        evaluation_frequency: Optional[int] = None,
+        gradient_accumulation_step: Optional[int] = None,
+        layers_to_freeze: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[Union[str, LearningRateScheduler]] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[float] = None,
+        nesterov: Optional[bool] = None,
+        number_of_epochs: Optional[int] = None,
+        number_of_workers: Optional[int] = None,
+        optimizer: Optional[Union[str, StochasticOptimizer]] = None,
+        random_seed: Optional[int] = None,
+        step_lr_gamma: Optional[float] = None,
+        step_lr_step_size: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_cosine_lr_cycles: Optional[float] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[int] = None,
+        weight_decay: Optional[float] = None,
+        training_crop_size: Optional[int] = None,
+        validation_crop_size: Optional[int] = None,
+        validation_resize_size: Optional[int] = None,
+        weighted_loss: Optional[int] = None,
+    ) -> None:
+        """Setting Image training parameters for AutoML Image Classification and Image Classification Multilabel tasks.
+
+        :keyword advanced_settings: Settings for advanced scenarios.
+        :paramtype advanced_settings: str
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: bool
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: float
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: float
+        :keyword checkpoint_frequency: Frequency to store model checkpoints. Must be a positive
+         integer.
+        :paramtype checkpoint_frequency: int
+        :keyword checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+         incremental training.
+        :paramtype checkpoint_run_id: str
+        :keyword distributed: Whether to use distributed training.
+        :paramtype distributed: bool
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: bool
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: int
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: int
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: bool
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: int
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: int
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/reference-automl-images-hyperparameters#model-agnostic-hyperparameters.   # pylint: disable=line-too-long
+        :type layers_to_freeze: int
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: float
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'. Possible values include: "None", "WarmupCosine", "Step".
+        :type learning_rate_scheduler: str or
+         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :type model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: float
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: bool
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: int
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: int
+        :keyword optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+        :type optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: int
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: float
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: int
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: int
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: int
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: float
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: int
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: float
+        :keyword training_crop_size: Image crop size that is input to the neural network for the
+         training dataset. Must be a positive integer.
+        :paramtype training_crop_size: int
+        :keyword validation_crop_size: Image crop size that is input to the neural network for the
+         validation dataset. Must be a positive integer.
+        :paramtype validation_crop_size: int
+        :keyword validation_resize_size: Image size to which to resize before cropping for validation
+         dataset. Must be a positive integer.
+        :paramtype validation_resize_size: int
+        :keyword weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+         1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+         0 or 1 or 2.
+        :paramtype weighted_loss: int
+        """
+        self._training_parameters = self._training_parameters or ImageModelSettingsClassification()
+
+        self._training_parameters.advanced_settings = (
+            advanced_settings if advanced_settings is not None else self._training_parameters.advanced_settings
+        )
+        self._training_parameters.ams_gradient = (
+            ams_gradient if ams_gradient is not None else self._training_parameters.ams_gradient
+        )
+        self._training_parameters.beta1 = beta1 if beta1 is not None else self._training_parameters.beta1
+        self._training_parameters.beta2 = beta2 if beta2 is not None else self._training_parameters.beta2
+        self._training_parameters.checkpoint_frequency = (
+            checkpoint_frequency if checkpoint_frequency is not None else self._training_parameters.checkpoint_frequency
+        )
+        self._training_parameters.checkpoint_run_id = (
+            checkpoint_run_id if checkpoint_run_id is not None else self._training_parameters.checkpoint_run_id
+        )
+        self._training_parameters.distributed = (
+            distributed if distributed is not None else self._training_parameters.distributed
+        )
+        self._training_parameters.early_stopping = (
+            early_stopping if early_stopping is not None else self._training_parameters.early_stopping
+        )
+        self._training_parameters.early_stopping_delay = (
+            early_stopping_delay if early_stopping_delay is not None else self._training_parameters.early_stopping_delay
+        )
+        self._training_parameters.early_stopping_patience = (
+            early_stopping_patience
+            if early_stopping_patience is not None
+            else self._training_parameters.early_stopping_patience
+        )
+        self._training_parameters.enable_onnx_normalization = (
+            enable_onnx_normalization
+            if enable_onnx_normalization is not None
+            else self._training_parameters.enable_onnx_normalization
+        )
+        self._training_parameters.evaluation_frequency = (
+            evaluation_frequency if evaluation_frequency is not None else self._training_parameters.evaluation_frequency
+        )
+        self._training_parameters.gradient_accumulation_step = (
+            gradient_accumulation_step
+            if gradient_accumulation_step is not None
+            else self._training_parameters.gradient_accumulation_step
+        )
+        self._training_parameters.layers_to_freeze = (
+            layers_to_freeze if layers_to_freeze is not None else self._training_parameters.layers_to_freeze
+        )
+        self._training_parameters.learning_rate = (
+            learning_rate if learning_rate is not None else self._training_parameters.learning_rate
+        )
+        self._training_parameters.learning_rate_scheduler = (
+            LearningRateScheduler[camel_to_snake(learning_rate_scheduler).upper()]
+            if learning_rate_scheduler is not None
+            else self._training_parameters.learning_rate_scheduler
+        )
+        self._training_parameters.model_name = (
+            model_name if model_name is not None else self._training_parameters.model_name
+        )
+        self._training_parameters.momentum = momentum if momentum is not None else self._training_parameters.momentum
+        self._training_parameters.nesterov = nesterov if nesterov is not None else self._training_parameters.nesterov
+        self._training_parameters.number_of_epochs = (
+            number_of_epochs if number_of_epochs is not None else self._training_parameters.number_of_epochs
+        )
+        self._training_parameters.number_of_workers = (
+            number_of_workers if number_of_workers is not None else self._training_parameters.number_of_workers
+        )
+        self._training_parameters.optimizer = (
+            StochasticOptimizer[camel_to_snake(optimizer).upper()]
+            if optimizer is not None
+            else self._training_parameters.optimizer
+        )
+        self._training_parameters.random_seed = (
+            random_seed if random_seed is not None else self._training_parameters.random_seed
+        )
+        self._training_parameters.step_lr_gamma = (
+            step_lr_gamma if step_lr_gamma is not None else self._training_parameters.step_lr_gamma
+        )
+        self._training_parameters.step_lr_step_size = (
+            step_lr_step_size if step_lr_step_size is not None else self._training_parameters.step_lr_step_size
+        )
+        self._training_parameters.training_batch_size = (
+            training_batch_size if training_batch_size is not None else self._training_parameters.training_batch_size
+        )
+        self._training_parameters.validation_batch_size = (
+            validation_batch_size
+            if validation_batch_size is not None
+            else self._training_parameters.validation_batch_size
+        )
+        self._training_parameters.warmup_cosine_lr_cycles = (
+            warmup_cosine_lr_cycles
+            if warmup_cosine_lr_cycles is not None
+            else self._training_parameters.warmup_cosine_lr_cycles
+        )
+        self._training_parameters.warmup_cosine_lr_warmup_epochs = (
+            warmup_cosine_lr_warmup_epochs
+            if warmup_cosine_lr_warmup_epochs is not None
+            else self._training_parameters.warmup_cosine_lr_warmup_epochs
+        )
+        self._training_parameters.weight_decay = (
+            weight_decay if weight_decay is not None else self._training_parameters.weight_decay
+        )
+        self._training_parameters.training_crop_size = (
+            training_crop_size if training_crop_size is not None else self._training_parameters.training_crop_size
+        )
+        self._training_parameters.validation_crop_size = (
+            validation_crop_size if validation_crop_size is not None else self._training_parameters.validation_crop_size
+        )
+        self._training_parameters.validation_resize_size = (
+            validation_resize_size
+            if validation_resize_size is not None
+            else self._training_parameters.validation_resize_size
+        )
+        self._training_parameters.weighted_loss = (
+            weighted_loss if weighted_loss is not None else self._training_parameters.weighted_loss
+        )
+
+    # pylint: enable=too-many-locals
+
+    def extend_search_space(
+        self,
+        value: Union[SearchSpace, List[SearchSpace]],
+    ) -> None:
+        """Add Search space for AutoML Image Classification and Image Classification Multilabel tasks.
+
+        :param value: specify either an instance of ImageClassificationSearchSpace or list of
+            ImageClassificationSearchSpace for searching through the parameter space
+        :type value: Union[ImageClassificationSearchSpace, List[ImageClassificationSearchSpace]]
+        """
+        self._search_space = self._search_space or []
+
+        if isinstance(value, list):
+            self._search_space.extend(
+                [
+                    cast_to_specific_search_space(item, ImageClassificationSearchSpace, self.task_type)  # type: ignore
+                    for item in value
+                ]
+            )
+        else:
+            self._search_space.append(
+                cast_to_specific_search_space(value, ImageClassificationSearchSpace, self.task_type)  # type: ignore
+            )
+
+    @classmethod
+    def _get_search_space_from_str(cls, search_space_str: str) -> Optional[List[ImageClassificationSearchSpace]]:
+        return (
+            [ImageClassificationSearchSpace._from_rest_object(entry) for entry in search_space_str if entry is not None]
+            if search_space_str is not None
+            else None
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, AutoMLImageClassificationBase):
+            return NotImplemented
+
+        if not super().__eq__(other):
+            return False
+
+        return self._training_parameters == other._training_parameters and self._search_space == other._search_space
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/automl_image_object_detection_base.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/automl_image_object_detection_base.py
new file mode 100644
index 00000000..db0c7bc6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/automl_image_object_detection_base.py
@@ -0,0 +1,524 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict, List, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    LearningRateScheduler,
+    LogTrainingMetrics,
+    LogValidationLoss,
+    ModelSize,
+    StochasticOptimizer,
+    ValidationMetricType,
+)
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.entities._job.automl import SearchSpace
+from azure.ai.ml.entities._job.automl.image.automl_image import AutoMLImage
+from azure.ai.ml.entities._job.automl.image.image_limit_settings import ImageLimitSettings
+from azure.ai.ml.entities._job.automl.image.image_model_settings import ImageModelSettingsObjectDetection
+from azure.ai.ml.entities._job.automl.image.image_object_detection_search_space import ImageObjectDetectionSearchSpace
+from azure.ai.ml.entities._job.automl.image.image_sweep_settings import ImageSweepSettings
+from azure.ai.ml.entities._job.automl.utils import cast_to_specific_search_space
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+class AutoMLImageObjectDetectionBase(AutoMLImage):
+    """Base class for AutoML Image Object Detection and Image Instance Segmentation tasks.
+
+    :keyword task_type: Type of task to run. Possible values include: "ImageObjectDetection",
+    "ImageInstanceSegmentation".
+    :paramtype task_type: str
+    :keyword limits: The resource limits for the job.
+    :paramtype limits: Optional[~azure.ai.ml.entities._job.automl.image.image_limit_settings.ImageLimitSettings]
+    :keyword sweep: The sweep settings for the job.
+    :paramtype sweep: Optional[~azure.ai.ml.entities._job.automl.image.image_sweep_settings.ImageSweepSettings]
+    :keyword training_parameters: The training parameters for the job.
+    :paramtype training_parameters: Optional[~azure.ai.ml.automl.ImageModelSettingsObjectDetection]
+    :keyword search_space: The search space for the job.
+    :paramtype search_space: Optional[List[~azure.ai.ml.automl.ImageObjectDetectionSearchSpace]]
+    """
+
+    def __init__(
+        self,
+        *,
+        task_type: str,
+        limits: Optional[ImageLimitSettings] = None,
+        sweep: Optional[ImageSweepSettings] = None,
+        training_parameters: Optional[ImageModelSettingsObjectDetection] = None,
+        search_space: Optional[List[ImageObjectDetectionSearchSpace]] = None,
+        **kwargs: Any,
+    ) -> None:
+        self._training_parameters: Optional[ImageModelSettingsObjectDetection] = None
+
+        super().__init__(task_type=task_type, limits=limits, sweep=sweep, **kwargs)
+
+        self.training_parameters = training_parameters  # Assigning training_parameters through setter method.
+
+        self._search_space = search_space
+
+    @property
+    def training_parameters(self) -> Optional[ImageModelSettingsObjectDetection]:
+        return self._training_parameters
+
+    @training_parameters.setter
+    def training_parameters(self, value: Union[Dict, ImageModelSettingsObjectDetection]) -> None:
+        if value is None:
+            self._training_parameters = None
+        elif isinstance(value, ImageModelSettingsObjectDetection):
+            self._training_parameters = value
+            # set_training_parameters convert parameter values from snake case str to enum.
+            # We need to add any future enum parameters in this call to support snake case str.
+            self.set_training_parameters(
+                optimizer=value.optimizer,
+                learning_rate_scheduler=value.learning_rate_scheduler,
+                model_size=value.model_size,
+                validation_metric_type=value.validation_metric_type,
+                log_training_metrics=value.log_training_metrics,
+                log_validation_loss=value.log_validation_loss,
+            )
+        elif value is None:
+            self._training_parameters = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for model settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_training_parameters(**value)
+
+    @property
+    def search_space(self) -> Optional[List[ImageObjectDetectionSearchSpace]]:
+        return self._search_space
+
+    @search_space.setter
+    def search_space(self, value: Union[List[Dict], List[SearchSpace]]) -> None:
+        if not isinstance(value, list):
+            msg = "Expected a list for search space."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+        all_dict_type = all(isinstance(item, dict) for item in value)
+        all_search_space_type = all(isinstance(item, SearchSpace) for item in value)
+
+        if all_search_space_type or all_dict_type:
+            self._search_space = [
+                cast_to_specific_search_space(item, ImageObjectDetectionSearchSpace, self.task_type)  # type: ignore
+                for item in value
+            ]
+        else:
+            msg = "Expected all items in the list to be either dictionaries or SearchSpace objects."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    # pylint: disable=too-many-locals
+    def set_training_parameters(
+        self,
+        *,
+        advanced_settings: Optional[str] = None,
+        ams_gradient: Optional[bool] = None,
+        beta1: Optional[float] = None,
+        beta2: Optional[float] = None,
+        checkpoint_frequency: Optional[int] = None,
+        checkpoint_run_id: Optional[str] = None,
+        distributed: Optional[bool] = None,
+        early_stopping: Optional[bool] = None,
+        early_stopping_delay: Optional[int] = None,
+        early_stopping_patience: Optional[int] = None,
+        enable_onnx_normalization: Optional[bool] = None,
+        evaluation_frequency: Optional[int] = None,
+        gradient_accumulation_step: Optional[int] = None,
+        layers_to_freeze: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[Union[str, LearningRateScheduler]] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[float] = None,
+        nesterov: Optional[bool] = None,
+        number_of_epochs: Optional[int] = None,
+        number_of_workers: Optional[int] = None,
+        optimizer: Optional[Union[str, StochasticOptimizer]] = None,
+        random_seed: Optional[int] = None,
+        step_lr_gamma: Optional[float] = None,
+        step_lr_step_size: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_cosine_lr_cycles: Optional[float] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[int] = None,
+        weight_decay: Optional[float] = None,
+        box_detections_per_image: Optional[int] = None,
+        box_score_threshold: Optional[float] = None,
+        image_size: Optional[int] = None,
+        max_size: Optional[int] = None,
+        min_size: Optional[int] = None,
+        model_size: Optional[Union[str, ModelSize]] = None,
+        multi_scale: Optional[bool] = None,
+        nms_iou_threshold: Optional[float] = None,
+        tile_grid_size: Optional[str] = None,
+        tile_overlap_ratio: Optional[float] = None,
+        tile_predictions_nms_threshold: Optional[float] = None,
+        validation_iou_threshold: Optional[float] = None,
+        validation_metric_type: Optional[Union[str, ValidationMetricType]] = None,
+        log_training_metrics: Optional[Union[str, LogTrainingMetrics]] = None,
+        log_validation_loss: Optional[Union[str, LogValidationLoss]] = None,
+    ) -> None:
+        """Setting Image training parameters for for AutoML Image Object Detection and Image Instance Segmentation
+        tasks.
+
+        :keyword advanced_settings: Settings for advanced scenarios.
+        :paramtype advanced_settings: str
+        :keyword ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+        :paramtype ams_gradient: bool
+        :keyword beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta1: float
+        :keyword beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+         range [0, 1].
+        :paramtype beta2: float
+        :keyword checkpoint_frequency: Frequency to store model checkpoints. Must be a positive
+         integer.
+        :paramtype checkpoint_frequency: int
+        :keyword checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+         incremental training.
+        :paramtype checkpoint_run_id: str
+        :keyword distributed: Whether to use distributed training.
+        :paramtype distributed: bool
+        :keyword early_stopping: Enable early stopping logic during training.
+        :paramtype early_stopping: bool
+        :keyword early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+         before primary metric improvement
+         is tracked for early stopping. Must be a positive integer.
+        :paramtype early_stopping_delay: int
+        :keyword early_stopping_patience: Minimum number of epochs or validation evaluations with no
+         primary metric improvement before
+         the run is stopped. Must be a positive integer.
+        :paramtype early_stopping_patience: int
+        :keyword enable_onnx_normalization: Enable normalization when exporting ONNX model.
+        :paramtype enable_onnx_normalization: bool
+        :keyword evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+         Must be a positive integer.
+        :paramtype evaluation_frequency: int
+        :keyword gradient_accumulation_step: Gradient accumulation means running a configured number of
+         "GradAccumulationStep" steps without
+         updating the model weights while accumulating the gradients of those steps, and then using
+         the accumulated gradients to compute the weight updates. Must be a positive integer.
+        :paramtype gradient_accumulation_step: int
+        :keyword layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+         integer.
+         For instance, passing 2 as value for 'seresnext' means
+         freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+         please
+         see: https://learn.microsoft.com/azure/machine-learning/reference-automl-images-hyperparameters#model-agnostic-hyperparameters.   # pylint: disable=line-too-long
+        :type layers_to_freeze: int
+        :keyword learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :paramtype learning_rate: float
+        :keyword learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+         'step'. Possible values include: "None", "WarmupCosine", "Step".
+        :type learning_rate_scheduler: str or
+         ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+        :keyword model_name: Name of the model to use for training.
+         For more information on the available models please visit the official documentation:
+         https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+        :type model_name: str
+        :keyword momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+         1].
+        :paramtype momentum: float
+        :keyword nesterov: Enable nesterov when optimizer is 'sgd'.
+        :paramtype nesterov: bool
+        :keyword number_of_epochs: Number of training epochs. Must be a positive integer.
+        :paramtype number_of_epochs: int
+        :keyword number_of_workers: Number of data loader workers. Must be a non-negative integer.
+        :paramtype number_of_workers: int
+        :keyword optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+        :type optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+        :keyword random_seed: Random seed to be used when using deterministic training.
+        :paramtype random_seed: int
+        :keyword step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+         in the range [0, 1].
+        :paramtype step_lr_gamma: float
+        :keyword step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+         a positive integer.
+        :paramtype step_lr_step_size: int
+        :keyword training_batch_size: Training batch size. Must be a positive integer.
+        :paramtype training_batch_size: int
+        :keyword validation_batch_size: Validation batch size. Must be a positive integer.
+        :paramtype validation_batch_size: int
+        :keyword warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+         'warmup_cosine'. Must be a float in the range [0, 1].
+        :paramtype warmup_cosine_lr_cycles: float
+        :keyword warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+         'warmup_cosine'. Must be a positive integer.
+        :paramtype warmup_cosine_lr_warmup_epochs: int
+        :keyword weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+         be a float in the range[0, 1].
+        :paramtype weight_decay: float
+        :keyword box_detections_per_image: Maximum number of detections per image, for all classes.
+         Must be a positive integer.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :type box_detections_per_image: int
+        :keyword box_score_threshold: During inference, only return proposals with a classification
+         score greater than
+         BoxScoreThreshold. Must be a float in the range[0, 1].
+        :paramtype box_score_threshold: float
+        :keyword image_size: Image size for training and validation. Must be a positive integer.
+         Note: The training run may get into CUDA OOM if the size is too big.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :type image_size: int
+        :keyword max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :type max_size: int
+        :keyword min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+         Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+         Note: This settings is not supported for the 'yolov5' algorithm.
+        :type min_size: int
+        :keyword model_size: Model size. Must be 'small', 'medium', 'large', or 'extra_large'.
+         Note: training run may get into CUDA OOM if the model size is too big.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :type model_size: str or ~azure.mgmt.machinelearningservices.models.ModelSize
+        :keyword multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+         Note: training run may get into CUDA OOM if no sufficient GPU memory.
+         Note: This settings is only supported for the 'yolov5' algorithm.
+        :type multi_scale: bool
+        :keyword nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
+         float in the range [0, 1].
+        :paramtype nms_iou_threshold: float
+        :keyword tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must
+         not be
+         None to enable small object detection logic. A string containing two integers in mxn format.
+        :type tile_grid_size: str
+        :keyword tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be
+         float in the range [0, 1).
+        :paramtype tile_overlap_ratio: float
+        :keyword tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+         predictions from tiles and image.
+         Used in validation/ inference. Must be float in the range [0, 1].
+         NMS: Non-maximum suppression.
+        :type tile_predictions_nms_threshold: str
+        :keyword validation_iou_threshold: IOU threshold to use when computing validation metric. Must
+         be float in the range [0, 1].
+        :paramtype validation_iou_threshold: float
+        :keyword validation_metric_type: Metric computation method to use for validation metrics. Must
+         be 'none', 'coco', 'voc', or 'coco_voc'.
+        :paramtype validation_metric_type: str or ~azure.mgmt.machinelearningservices.models.ValidationMetricType
+        :keyword log_training_metrics: indicates whether or not to log training metrics. Must
+         be 'Enable' or 'Disable'
+        :paramtype log_training_metrics: str or ~azure.mgmt.machinelearningservices.models.LogTrainingMetrics
+        :keyword log_validation_loss: indicates whether or not to log validation loss. Must
+         be 'Enable' or 'Disable'
+        :paramtype log_validation_loss: str or ~azure.mgmt.machinelearningservices.models.LogValidationLoss
+        """
+        self._training_parameters = self._training_parameters or ImageModelSettingsObjectDetection()
+
+        self._training_parameters.advanced_settings = (
+            advanced_settings if advanced_settings is not None else self._training_parameters.advanced_settings
+        )
+        self._training_parameters.ams_gradient = (
+            ams_gradient if ams_gradient is not None else self._training_parameters.ams_gradient
+        )
+        self._training_parameters.beta1 = beta1 if beta1 is not None else self._training_parameters.beta1
+        self._training_parameters.beta2 = beta2 if beta2 is not None else self._training_parameters.beta2
+        self._training_parameters.checkpoint_frequency = (
+            checkpoint_frequency if checkpoint_frequency is not None else self._training_parameters.checkpoint_frequency
+        )
+        self._training_parameters.checkpoint_run_id = (
+            checkpoint_run_id if checkpoint_run_id is not None else self._training_parameters.checkpoint_run_id
+        )
+        self._training_parameters.distributed = (
+            distributed if distributed is not None else self._training_parameters.distributed
+        )
+        self._training_parameters.early_stopping = (
+            early_stopping if early_stopping is not None else self._training_parameters.early_stopping
+        )
+        self._training_parameters.early_stopping_delay = (
+            early_stopping_delay if early_stopping_delay is not None else self._training_parameters.early_stopping_delay
+        )
+        self._training_parameters.early_stopping_patience = (
+            early_stopping_patience
+            if early_stopping_patience is not None
+            else self._training_parameters.early_stopping_patience
+        )
+        self._training_parameters.enable_onnx_normalization = (
+            enable_onnx_normalization
+            if enable_onnx_normalization is not None
+            else self._training_parameters.enable_onnx_normalization
+        )
+        self._training_parameters.evaluation_frequency = (
+            evaluation_frequency if evaluation_frequency is not None else self._training_parameters.evaluation_frequency
+        )
+        self._training_parameters.gradient_accumulation_step = (
+            gradient_accumulation_step
+            if gradient_accumulation_step is not None
+            else self._training_parameters.gradient_accumulation_step
+        )
+        self._training_parameters.layers_to_freeze = (
+            layers_to_freeze if layers_to_freeze is not None else self._training_parameters.layers_to_freeze
+        )
+        self._training_parameters.learning_rate = (
+            learning_rate if learning_rate is not None else self._training_parameters.learning_rate
+        )
+        self._training_parameters.learning_rate_scheduler = (
+            LearningRateScheduler[camel_to_snake(learning_rate_scheduler)]
+            if learning_rate_scheduler is not None
+            else self._training_parameters.learning_rate_scheduler
+        )
+        self._training_parameters.model_name = (
+            model_name if model_name is not None else self._training_parameters.model_name
+        )
+        self._training_parameters.momentum = momentum if momentum is not None else self._training_parameters.momentum
+        self._training_parameters.nesterov = nesterov if nesterov is not None else self._training_parameters.nesterov
+        self._training_parameters.number_of_epochs = (
+            number_of_epochs if number_of_epochs is not None else self._training_parameters.number_of_epochs
+        )
+        self._training_parameters.number_of_workers = (
+            number_of_workers if number_of_workers is not None else self._training_parameters.number_of_workers
+        )
+        self._training_parameters.optimizer = (
+            StochasticOptimizer[camel_to_snake(optimizer)]
+            if optimizer is not None
+            else self._training_parameters.optimizer
+        )
+        self._training_parameters.random_seed = (
+            random_seed if random_seed is not None else self._training_parameters.random_seed
+        )
+        self._training_parameters.step_lr_gamma = (
+            step_lr_gamma if step_lr_gamma is not None else self._training_parameters.step_lr_gamma
+        )
+        self._training_parameters.step_lr_step_size = (
+            step_lr_step_size if step_lr_step_size is not None else self._training_parameters.step_lr_step_size
+        )
+        self._training_parameters.training_batch_size = (
+            training_batch_size if training_batch_size is not None else self._training_parameters.training_batch_size
+        )
+        self._training_parameters.validation_batch_size = (
+            validation_batch_size
+            if validation_batch_size is not None
+            else self._training_parameters.validation_batch_size
+        )
+        self._training_parameters.warmup_cosine_lr_cycles = (
+            warmup_cosine_lr_cycles
+            if warmup_cosine_lr_cycles is not None
+            else self._training_parameters.warmup_cosine_lr_cycles
+        )
+        self._training_parameters.warmup_cosine_lr_warmup_epochs = (
+            warmup_cosine_lr_warmup_epochs
+            if warmup_cosine_lr_warmup_epochs is not None
+            else self._training_parameters.warmup_cosine_lr_warmup_epochs
+        )
+        self._training_parameters.weight_decay = (
+            weight_decay if weight_decay is not None else self._training_parameters.weight_decay
+        )
+        self._training_parameters.box_detections_per_image = (
+            box_detections_per_image
+            if box_detections_per_image is not None
+            else self._training_parameters.box_detections_per_image
+        )
+        self._training_parameters.box_score_threshold = (
+            box_score_threshold if box_score_threshold is not None else self._training_parameters.box_score_threshold
+        )
+        self._training_parameters.image_size = (
+            image_size if image_size is not None else self._training_parameters.image_size
+        )
+        self._training_parameters.max_size = max_size if max_size is not None else self._training_parameters.max_size
+        self._training_parameters.min_size = min_size if min_size is not None else self._training_parameters.min_size
+        self._training_parameters.model_size = (
+            ModelSize[camel_to_snake(model_size)] if model_size is not None else self._training_parameters.model_size
+        )
+        self._training_parameters.multi_scale = (
+            multi_scale if multi_scale is not None else self._training_parameters.multi_scale
+        )
+        self._training_parameters.nms_iou_threshold = (
+            nms_iou_threshold if nms_iou_threshold is not None else self._training_parameters.nms_iou_threshold
+        )
+        self._training_parameters.tile_grid_size = (
+            tile_grid_size if tile_grid_size is not None else self._training_parameters.tile_grid_size
+        )
+        self._training_parameters.tile_overlap_ratio = (
+            tile_overlap_ratio if tile_overlap_ratio is not None else self._training_parameters.tile_overlap_ratio
+        )
+        self._training_parameters.tile_predictions_nms_threshold = (
+            tile_predictions_nms_threshold
+            if tile_predictions_nms_threshold is not None
+            else self._training_parameters.tile_predictions_nms_threshold
+        )
+        self._training_parameters.validation_iou_threshold = (
+            validation_iou_threshold
+            if validation_iou_threshold is not None
+            else self._training_parameters.validation_iou_threshold
+        )
+        self._training_parameters.validation_metric_type = (
+            ValidationMetricType[camel_to_snake(validation_metric_type)]
+            if validation_metric_type is not None
+            else self._training_parameters.validation_metric_type
+        )
+        self._training_parameters.log_training_metrics = (
+            LogTrainingMetrics[camel_to_snake(log_training_metrics)]
+            if log_training_metrics is not None
+            else self._training_parameters.log_training_metrics
+        )
+        self._training_parameters.log_validation_loss = (
+            LogValidationLoss[camel_to_snake(log_validation_loss)]
+            if log_validation_loss is not None
+            else self._training_parameters.log_validation_loss
+        )
+
+    # pylint: enable=too-many-locals
+
+    def extend_search_space(
+        self,
+        value: Union[SearchSpace, List[SearchSpace]],
+    ) -> None:
+        """Add search space for AutoML Image Object Detection and Image Instance Segmentation tasks.
+
+        :param value: Search through the parameter space
+        :type value: Union[SearchSpace, List[SearchSpace]]
+        """
+        self._search_space = self._search_space or []
+
+        if isinstance(value, list):
+            self._search_space.extend(
+                [
+                    cast_to_specific_search_space(item, ImageObjectDetectionSearchSpace, self.task_type)  # type: ignore
+                    for item in value
+                ]
+            )
+        else:
+            self._search_space.append(
+                cast_to_specific_search_space(value, ImageObjectDetectionSearchSpace, self.task_type)  # type: ignore
+            )
+
+    @classmethod
+    def _get_search_space_from_str(cls, search_space_str: str) -> Optional[List[ImageObjectDetectionSearchSpace]]:
+        return (
+            [
+                ImageObjectDetectionSearchSpace._from_rest_object(entry)
+                for entry in search_space_str
+                if entry is not None
+            ]
+            if search_space_str is not None
+            else None
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, AutoMLImageObjectDetectionBase):
+            return NotImplemented
+
+        if not super().__eq__(other):
+            return False
+
+        return self._training_parameters == other._training_parameters and self._search_space == other._search_space
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_classification_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_classification_job.py
new file mode 100644
index 00000000..a1b9dbc3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_classification_job.py
@@ -0,0 +1,244 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ClassificationPrimaryMetrics
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ImageClassification as RestImageClassification
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase, TaskType
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.image.automl_image_classification_base import AutoMLImageClassificationBase
+from azure.ai.ml.entities._job.automl.image.image_limit_settings import ImageLimitSettings
+from azure.ai.ml.entities._job.automl.image.image_model_settings import ImageModelSettingsClassification
+from azure.ai.ml.entities._job.automl.image.image_sweep_settings import ImageSweepSettings
+from azure.ai.ml.entities._util import load_from_dict
+
+
+class ImageClassificationJob(AutoMLImageClassificationBase):
+    """Configuration for AutoML multi-class Image Classification job.
+
+    :param primary_metric: The primary metric to use for optimization.
+    :type primary_metric: Optional[str, ~azure.ai.ml.automl.ClassificationMultilabelPrimaryMetrics]
+    :param kwargs: Job-specific arguments.
+    :type kwargs: Dict[str, Any]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+            :start-after: [START automl.automl_image_job.image_classification_job]
+            :end-before: [END automl.automl_image_job.image_classification_job]
+            :language: python
+            :dedent: 8
+            :caption: creating an automl image classification job
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ClassificationPrimaryMetrics.ACCURACY
+
+    def __init__(
+        self,
+        *,
+        primary_metric: Optional[Union[str, ClassificationPrimaryMetrics]] = None,
+        **kwargs: Any,
+    ) -> None:
+
+        # Extract any super class init settings
+        limits = kwargs.pop("limits", None)
+        sweep = kwargs.pop("sweep", None)
+        training_parameters = kwargs.pop("training_parameters", None)
+        search_space = kwargs.pop("search_space", None)
+
+        super().__init__(
+            task_type=TaskType.IMAGE_CLASSIFICATION,
+            limits=limits,
+            sweep=sweep,
+            training_parameters=training_parameters,
+            search_space=search_space,
+            **kwargs,
+        )
+
+        self.primary_metric = primary_metric or ImageClassificationJob._DEFAULT_PRIMARY_METRIC
+
+    @property
+    def primary_metric(self) -> Optional[Union[str, ClassificationPrimaryMetrics]]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ClassificationPrimaryMetrics]) -> None:
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+        self._primary_metric = (
+            ImageClassificationJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ClassificationPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        image_classification_task = RestImageClassification(
+            target_column_name=self.target_column_name,
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            validation_data_size=self.validation_data_size,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            sweep_settings=self._sweep._to_rest_object() if self._sweep else None,
+            model_settings=self._training_parameters._to_rest_object() if self._training_parameters else None,
+            search_space=(
+                [entry._to_rest_object() for entry in self._search_space if entry is not None]
+                if self._search_space is not None
+                else None
+            ),
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        # resolve data inputs in rest obj
+        self._resolve_data_inputs(image_classification_task)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=image_classification_task,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "ImageClassificationJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestImageClassification = properties.task_details
+
+        job_args_dict = {
+            "id": obj.id,
+            "name": obj.name,
+            "description": properties.description,
+            "tags": properties.tags,
+            "properties": properties.properties,
+            "experiment_name": properties.experiment_name,
+            "services": properties.services,
+            "status": properties.status,
+            "creation_context": obj.system_data,
+            "display_name": properties.display_name,
+            "compute": properties.compute_id,
+            "outputs": from_rest_data_outputs(properties.outputs),
+            "resources": properties.resources,
+            "identity": (
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            "queue_settings": properties.queue_settings,
+        }
+
+        image_classification_job = cls(
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            validation_data_size=task_details.validation_data_size,
+            limits=(
+                ImageLimitSettings._from_rest_object(task_details.limit_settings)
+                if task_details.limit_settings
+                else None
+            ),
+            sweep=(
+                ImageSweepSettings._from_rest_object(task_details.sweep_settings)
+                if task_details.sweep_settings
+                else None
+            ),
+            training_parameters=(
+                ImageModelSettingsClassification._from_rest_object(task_details.model_settings)
+                if task_details.model_settings
+                else None
+            ),
+            search_space=cls._get_search_space_from_str(task_details.search_space),
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            **job_args_dict,
+        )
+
+        image_classification_job._restore_data_inputs()
+
+        return image_classification_job
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "ImageClassificationJob":
+        from azure.ai.ml._schema.automl.image_vertical.image_classification import ImageClassificationSchema
+        from azure.ai.ml._schema.pipeline.automl_node import ImageClassificationMulticlassNodeSchema
+
+        inside_pipeline = kwargs.pop("inside_pipeline", False)
+        if inside_pipeline:
+            if context.get("inside_pipeline", None) is None:
+                context["inside_pipeline"] = True
+            loaded_data = load_from_dict(
+                ImageClassificationMulticlassNodeSchema,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        else:
+            loaded_data = load_from_dict(ImageClassificationSchema, data, context, additional_message, **kwargs)
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "ImageClassificationJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        data_settings = {
+            "training_data": loaded_data.pop("training_data"),
+            "target_column_name": loaded_data.pop("target_column_name"),
+            "validation_data": loaded_data.pop("validation_data", None),
+            "validation_data_size": loaded_data.pop("validation_data_size", None),
+        }
+        job = ImageClassificationJob(**loaded_data)
+        job.set_data(**data_settings)
+        return job
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.image_vertical.image_classification import ImageClassificationSchema
+        from azure.ai.ml._schema.pipeline.automl_node import ImageClassificationMulticlassNodeSchema
+
+        schema_dict: dict = {}
+        if inside_pipeline:
+            schema_dict = ImageClassificationMulticlassNodeSchema(
+                context={BASE_PATH_CONTEXT_KEY: "./", "inside_pipeline": True}
+            ).dump(self)
+        else:
+            schema_dict = ImageClassificationSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+
+        return schema_dict
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ImageClassificationJob):
+            return NotImplemented
+
+        if not super().__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_classification_multilabel_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_classification_multilabel_job.py
new file mode 100644
index 00000000..541f41c7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_classification_multilabel_job.py
@@ -0,0 +1,252 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ClassificationMultilabelPrimaryMetrics
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    ImageClassificationMultilabel as RestImageClassificationMultilabel,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase, TaskType
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.image.automl_image_classification_base import AutoMLImageClassificationBase
+from azure.ai.ml.entities._job.automl.image.image_limit_settings import ImageLimitSettings
+from azure.ai.ml.entities._job.automl.image.image_model_settings import ImageModelSettingsClassification
+from azure.ai.ml.entities._job.automl.image.image_sweep_settings import ImageSweepSettings
+from azure.ai.ml.entities._util import load_from_dict
+
+
+class ImageClassificationMultilabelJob(AutoMLImageClassificationBase):
+    """Configuration for AutoML multi-label Image Classification job.
+
+    :param primary_metric: The primary metric to use for optimization.
+    :type primary_metric: Optional[str, ~azure.ai.ml.automl.ClassificationMultilabelPrimaryMetrics]
+    :param kwargs: Job-specific arguments.
+    :type kwargs: Dict[str, Any]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+            :start-after: [START automl.automl_image_job.image_classification_multilabel_job]
+            :end-before: [END automl.automl_image_job.image_classification_multilabel_job]
+            :language: python
+            :dedent: 8
+            :caption: creating an automl image classification multilabel job
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ClassificationMultilabelPrimaryMetrics.IOU
+
+    def __init__(
+        self,
+        *,
+        primary_metric: Optional[Union[str, ClassificationMultilabelPrimaryMetrics]] = None,
+        **kwargs: Any,
+    ) -> None:
+
+        # Extract any super class init settings
+        limits = kwargs.pop("limits", None)
+        sweep = kwargs.pop("sweep", None)
+        training_parameters = kwargs.pop("training_parameters", None)
+        search_space = kwargs.pop("search_space", None)
+
+        super().__init__(
+            task_type=TaskType.IMAGE_CLASSIFICATION_MULTILABEL,
+            limits=limits,
+            sweep=sweep,
+            training_parameters=training_parameters,
+            search_space=search_space,
+            **kwargs,
+        )
+
+        self.primary_metric = primary_metric or ImageClassificationMultilabelJob._DEFAULT_PRIMARY_METRIC
+
+    @property
+    def primary_metric(self) -> Union[str, ClassificationMultilabelPrimaryMetrics]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ClassificationMultilabelPrimaryMetrics]) -> None:
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+        self._primary_metric = (
+            ImageClassificationMultilabelJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ClassificationMultilabelPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        image_classification_multilabel_task = RestImageClassificationMultilabel(
+            target_column_name=self.target_column_name,
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            validation_data_size=self.validation_data_size,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            sweep_settings=self._sweep._to_rest_object() if self._sweep else None,
+            model_settings=self._training_parameters._to_rest_object() if self._training_parameters else None,
+            search_space=(
+                [entry._to_rest_object() for entry in self._search_space if entry is not None]
+                if self._search_space is not None
+                else None
+            ),
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        # resolve data inputs in rest obj
+        self._resolve_data_inputs(image_classification_multilabel_task)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=image_classification_multilabel_task,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "ImageClassificationMultilabelJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestImageClassificationMultilabel = properties.task_details
+
+        job_args_dict = {
+            "id": obj.id,
+            "name": obj.name,
+            "description": properties.description,
+            "tags": properties.tags,
+            "properties": properties.properties,
+            "experiment_name": properties.experiment_name,
+            "services": properties.services,
+            "status": properties.status,
+            "creation_context": obj.system_data,
+            "display_name": properties.display_name,
+            "compute": properties.compute_id,
+            "outputs": from_rest_data_outputs(properties.outputs),
+            "resources": properties.resources,
+            "identity": (
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            "queue_settings": properties.queue_settings,
+        }
+
+        image_classification_multilabel_job = cls(
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            validation_data_size=task_details.validation_data_size,
+            limits=(
+                ImageLimitSettings._from_rest_object(task_details.limit_settings)
+                if task_details.limit_settings
+                else None
+            ),
+            sweep=(
+                ImageSweepSettings._from_rest_object(task_details.sweep_settings)
+                if task_details.sweep_settings
+                else None
+            ),
+            training_parameters=(
+                ImageModelSettingsClassification._from_rest_object(task_details.model_settings)
+                if task_details.model_settings
+                else None
+            ),
+            search_space=cls._get_search_space_from_str(task_details.search_space),
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            **job_args_dict,
+        )
+
+        image_classification_multilabel_job._restore_data_inputs()
+
+        return image_classification_multilabel_job
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "ImageClassificationMultilabelJob":
+        from azure.ai.ml._schema.automl.image_vertical.image_classification import ImageClassificationMultilabelSchema
+        from azure.ai.ml._schema.pipeline.automl_node import ImageClassificationMultilabelNodeSchema
+
+        inside_pipeline = kwargs.pop("inside_pipeline", False)
+        if inside_pipeline:
+            if context.get("inside_pipeline", None) is None:
+                context["inside_pipeline"] = True
+            loaded_data = load_from_dict(
+                ImageClassificationMultilabelNodeSchema,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        else:
+            loaded_data = load_from_dict(
+                ImageClassificationMultilabelSchema,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "ImageClassificationMultilabelJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        data_settings = {
+            "training_data": loaded_data.pop("training_data"),
+            "target_column_name": loaded_data.pop("target_column_name"),
+            "validation_data": loaded_data.pop("validation_data", None),
+            "validation_data_size": loaded_data.pop("validation_data_size", None),
+        }
+        job = ImageClassificationMultilabelJob(**loaded_data)
+        job.set_data(**data_settings)
+        return job
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.image_vertical.image_classification import ImageClassificationMultilabelSchema
+        from azure.ai.ml._schema.pipeline.automl_node import ImageClassificationMultilabelNodeSchema
+
+        schema_dict: dict = {}
+        if inside_pipeline:
+            schema_dict = ImageClassificationMultilabelNodeSchema(
+                context={BASE_PATH_CONTEXT_KEY: "./", "inside_pipeline": True}
+            ).dump(self)
+        else:
+            schema_dict = ImageClassificationMultilabelSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+
+        return schema_dict
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ImageClassificationMultilabelJob):
+            return NotImplemented
+
+        if not super().__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_classification_search_space.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_classification_search_space.py
new file mode 100644
index 00000000..0691f243
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_classification_search_space.py
@@ -0,0 +1,437 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=R0902,too-many-locals
+
+from typing import Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ImageModelDistributionSettingsClassification
+from azure.ai.ml.entities._job.automl.search_space import SearchSpace
+from azure.ai.ml.entities._job.automl.search_space_utils import _convert_from_rest_object, _convert_to_rest_object
+from azure.ai.ml.entities._job.sweep.search_space import SweepDistribution
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class ImageClassificationSearchSpace(RestTranslatableMixin):
+    """Search space for AutoML Image Classification and Image Classification
+    Multilabel tasks.
+
+    :param ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :type ams_gradient: bool or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+        range [0, 1].
+    :type beta1: float or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+        range [0, 1].
+    :type beta2: float or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param distributed: Whether to use distributer training.
+    :type distributed: bool or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param early_stopping: Enable early stopping logic during training.
+    :type early_stopping: bool or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+        before primary metric improvement
+        is tracked for early stopping. Must be a positive integer.
+    :type early_stopping_delay: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param early_stopping_patience: Minimum number of epochs or validation evaluations with no
+        primary metric improvement before
+        the run is stopped. Must be a positive integer.
+    :type early_stopping_patience: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :type enable_onnx_normalization: bool or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+        Must be a positive integer.
+    :type evaluation_frequency: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param gradient_accumulation_step: Gradient accumulation means running a configured number of
+        "GradAccumulationStep" steps without
+        updating the model weights while accumulating the gradients of those steps, and then using
+        the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :type gradient_accumulation_step: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+        integer.
+        For instance, passing 2 as value for 'seresnext' means
+        freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+        please
+        see: https://learn.microsoft.com/azure/machine-learning/reference-automl-images-hyperparameters#model-agnostic-hyperparameters.    # pylint: disable=line-too-long
+    :type layers_to_freeze: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :type learning_rate: float or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+        'step'.
+    :type learning_rate_scheduler: str or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param model_name: Name of the model to use for training.
+        For more information on the available models please visit the official documentation:
+        https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :type model_name: str or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+        1].
+    :type momentum: float or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param nesterov: Enable nesterov when optimizer is 'sgd'.
+    :type nesterov: bool or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param number_of_epochs: Number of training epochs. Must be a positive integer.
+    :type number_of_epochs: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :type number_of_workers: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :type optimizer: str or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param random_seed: Random seed to be used when using deterministic training.
+    :type random_seed: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+        in the range [0, 1].
+    :type step_lr_gamma: float or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+        a positive integer.
+    :type step_lr_step_size: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param training_batch_size: Training batch size. Must be a positive integer.
+    :type training_batch_size: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param validation_batch_size: Validation batch size. Must be a positive integer.
+    :type validation_batch_size: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+        'warmup_cosine'. Must be a float in the range [0, 1].
+    :type warmup_cosine_lr_cycles: float or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+        'warmup_cosine'. Must be a positive integer.
+    :type warmup_cosine_lr_warmup_epochs: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+        be a float in the range[0, 1].
+    :type weight_decay: float or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param training_crop_size: Image crop size that is input to the neural network for the
+        training dataset. Must be a positive integer.
+    :type training_crop_size: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param validation_crop_size: Image crop size that is input to the neural network for the
+        validation dataset. Must be a positive integer.
+    :type validation_crop_size: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param validation_resize_size: Image size to which to resize before cropping for validation
+        dataset. Must be a positive integer.
+    :type validation_resize_size: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+    :param weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+        1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+        0 or 1 or 2.
+    :type weighted_loss: int or ~azure.ai.ml.entities._job.sweep.search_space.SweepDistribution
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+            :start-after: [START automl.automl_image_job.image_classification_search_space]
+            :end-before: [END automl.automl_image_job.image_classification_search_space]
+            :language: python
+            :dedent: 8
+            :caption: Defining an automl image classification search space
+    """
+
+    def __init__(
+        self,
+        *,
+        ams_gradient: Optional[Union[bool, SweepDistribution]] = None,
+        beta1: Optional[Union[float, SweepDistribution]] = None,
+        beta2: Optional[Union[float, SweepDistribution]] = None,
+        distributed: Optional[Union[bool, SweepDistribution]] = None,
+        early_stopping: Optional[Union[bool, SweepDistribution]] = None,
+        early_stopping_delay: Optional[Union[int, SweepDistribution]] = None,
+        early_stopping_patience: Optional[Union[int, SweepDistribution]] = None,
+        enable_onnx_normalization: Optional[Union[bool, SweepDistribution]] = None,
+        evaluation_frequency: Optional[Union[int, SweepDistribution]] = None,
+        gradient_accumulation_step: Optional[Union[int, SweepDistribution]] = None,
+        layers_to_freeze: Optional[Union[int, SweepDistribution]] = None,
+        learning_rate: Optional[Union[float, SweepDistribution]] = None,
+        learning_rate_scheduler: Optional[Union[str, SweepDistribution]] = None,
+        model_name: Optional[Union[str, SweepDistribution]] = None,
+        momentum: Optional[Union[float, SweepDistribution]] = None,
+        nesterov: Optional[Union[bool, SweepDistribution]] = None,
+        number_of_epochs: Optional[Union[int, SweepDistribution]] = None,
+        number_of_workers: Optional[Union[int, SweepDistribution]] = None,
+        optimizer: Optional[Union[str, SweepDistribution]] = None,
+        random_seed: Optional[Union[int, SweepDistribution]] = None,
+        step_lr_gamma: Optional[Union[float, SweepDistribution]] = None,
+        step_lr_step_size: Optional[Union[int, SweepDistribution]] = None,
+        training_batch_size: Optional[Union[int, SweepDistribution]] = None,
+        validation_batch_size: Optional[Union[int, SweepDistribution]] = None,
+        warmup_cosine_lr_cycles: Optional[Union[float, SweepDistribution]] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[Union[int, SweepDistribution]] = None,
+        weight_decay: Optional[Union[float, SweepDistribution]] = None,
+        training_crop_size: Optional[Union[int, SweepDistribution]] = None,
+        validation_crop_size: Optional[Union[int, SweepDistribution]] = None,
+        validation_resize_size: Optional[Union[int, SweepDistribution]] = None,
+        weighted_loss: Optional[Union[int, SweepDistribution]] = None,
+    ) -> None:
+        self.ams_gradient = ams_gradient
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.distributed = distributed
+        self.early_stopping = early_stopping
+        self.early_stopping_delay = early_stopping_delay
+        self.early_stopping_patience = early_stopping_patience
+        self.enable_onnx_normalization = enable_onnx_normalization
+        self.evaluation_frequency = evaluation_frequency
+        self.gradient_accumulation_step = gradient_accumulation_step
+        self.layers_to_freeze = layers_to_freeze
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.momentum = momentum
+        self.nesterov = nesterov
+        self.number_of_epochs = number_of_epochs
+        self.number_of_workers = number_of_workers
+        self.optimizer = optimizer
+        self.random_seed = random_seed
+        self.step_lr_gamma = step_lr_gamma
+        self.step_lr_step_size = step_lr_step_size
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_cosine_lr_cycles = warmup_cosine_lr_cycles
+        self.warmup_cosine_lr_warmup_epochs = warmup_cosine_lr_warmup_epochs
+        self.weight_decay = weight_decay
+        self.training_crop_size = training_crop_size
+        self.validation_crop_size = validation_crop_size
+        self.validation_resize_size = validation_resize_size
+        self.weighted_loss = weighted_loss
+
+    def _to_rest_object(self) -> ImageModelDistributionSettingsClassification:
+        return ImageModelDistributionSettingsClassification(
+            ams_gradient=_convert_to_rest_object(self.ams_gradient) if self.ams_gradient is not None else None,
+            beta1=_convert_to_rest_object(self.beta1) if self.beta1 is not None else None,
+            beta2=_convert_to_rest_object(self.beta2) if self.beta2 is not None else None,
+            distributed=_convert_to_rest_object(self.distributed) if self.distributed is not None else None,
+            early_stopping=_convert_to_rest_object(self.early_stopping) if self.early_stopping is not None else None,
+            early_stopping_delay=(
+                _convert_to_rest_object(self.early_stopping_delay) if self.early_stopping_delay is not None else None
+            ),
+            early_stopping_patience=(
+                _convert_to_rest_object(self.early_stopping_patience)
+                if self.early_stopping_patience is not None
+                else None
+            ),
+            enable_onnx_normalization=(
+                _convert_to_rest_object(self.enable_onnx_normalization)
+                if self.enable_onnx_normalization is not None
+                else None
+            ),
+            evaluation_frequency=(
+                _convert_to_rest_object(self.evaluation_frequency) if self.evaluation_frequency is not None else None
+            ),
+            gradient_accumulation_step=(
+                _convert_to_rest_object(self.gradient_accumulation_step)
+                if self.gradient_accumulation_step is not None
+                else None
+            ),
+            layers_to_freeze=(
+                _convert_to_rest_object(self.layers_to_freeze) if self.layers_to_freeze is not None else None
+            ),
+            learning_rate=_convert_to_rest_object(self.learning_rate) if self.learning_rate is not None else None,
+            learning_rate_scheduler=(
+                _convert_to_rest_object(self.learning_rate_scheduler)
+                if self.learning_rate_scheduler is not None
+                else None
+            ),
+            model_name=_convert_to_rest_object(self.model_name) if self.model_name is not None else None,
+            momentum=_convert_to_rest_object(self.momentum) if self.momentum is not None else None,
+            nesterov=_convert_to_rest_object(self.nesterov) if self.nesterov is not None else None,
+            number_of_epochs=(
+                _convert_to_rest_object(self.number_of_epochs) if self.number_of_epochs is not None else None
+            ),
+            number_of_workers=(
+                _convert_to_rest_object(self.number_of_workers) if self.number_of_workers is not None else None
+            ),
+            optimizer=_convert_to_rest_object(self.optimizer) if self.optimizer is not None else None,
+            random_seed=_convert_to_rest_object(self.random_seed) if self.random_seed is not None else None,
+            step_lr_gamma=_convert_to_rest_object(self.step_lr_gamma) if self.step_lr_gamma is not None else None,
+            step_lr_step_size=(
+                _convert_to_rest_object(self.step_lr_step_size) if self.step_lr_step_size is not None else None
+            ),
+            training_batch_size=(
+                _convert_to_rest_object(self.training_batch_size) if self.training_batch_size is not None else None
+            ),
+            validation_batch_size=(
+                _convert_to_rest_object(self.validation_batch_size) if self.validation_batch_size is not None else None
+            ),
+            warmup_cosine_lr_cycles=(
+                _convert_to_rest_object(self.warmup_cosine_lr_cycles)
+                if self.warmup_cosine_lr_cycles is not None
+                else None
+            ),
+            warmup_cosine_lr_warmup_epochs=(
+                _convert_to_rest_object(self.warmup_cosine_lr_warmup_epochs)
+                if self.warmup_cosine_lr_warmup_epochs is not None
+                else None
+            ),
+            weight_decay=_convert_to_rest_object(self.weight_decay) if self.weight_decay is not None else None,
+            training_crop_size=(
+                _convert_to_rest_object(self.training_crop_size) if self.training_crop_size is not None else None
+            ),
+            validation_crop_size=(
+                _convert_to_rest_object(self.validation_crop_size) if self.validation_crop_size is not None else None
+            ),
+            validation_resize_size=(
+                _convert_to_rest_object(self.validation_resize_size)
+                if self.validation_resize_size is not None
+                else None
+            ),
+            weighted_loss=_convert_to_rest_object(self.weighted_loss) if self.weighted_loss is not None else None,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: ImageModelDistributionSettingsClassification) -> "ImageClassificationSearchSpace":
+        return cls(
+            ams_gradient=_convert_from_rest_object(obj.ams_gradient) if obj.ams_gradient is not None else None,
+            beta1=_convert_from_rest_object(obj.beta1) if obj.beta1 is not None else None,
+            beta2=_convert_from_rest_object(obj.beta2) if obj.beta2 is not None else None,
+            distributed=_convert_from_rest_object(obj.distributed) if obj.distributed is not None else None,
+            early_stopping=_convert_from_rest_object(obj.early_stopping) if obj.early_stopping is not None else None,
+            early_stopping_delay=(
+                _convert_from_rest_object(obj.early_stopping_delay) if obj.early_stopping_delay is not None else None
+            ),
+            early_stopping_patience=(
+                _convert_from_rest_object(obj.early_stopping_patience)
+                if obj.early_stopping_patience is not None
+                else None
+            ),
+            enable_onnx_normalization=(
+                _convert_from_rest_object(obj.enable_onnx_normalization)
+                if obj.enable_onnx_normalization is not None
+                else None
+            ),
+            evaluation_frequency=(
+                _convert_from_rest_object(obj.evaluation_frequency) if obj.evaluation_frequency is not None else None
+            ),
+            gradient_accumulation_step=(
+                _convert_from_rest_object(obj.gradient_accumulation_step)
+                if obj.gradient_accumulation_step is not None
+                else None
+            ),
+            layers_to_freeze=(
+                _convert_from_rest_object(obj.layers_to_freeze) if obj.layers_to_freeze is not None else None
+            ),
+            learning_rate=_convert_from_rest_object(obj.learning_rate) if obj.learning_rate is not None else None,
+            learning_rate_scheduler=(
+                _convert_from_rest_object(obj.learning_rate_scheduler)
+                if obj.learning_rate_scheduler is not None
+                else None
+            ),
+            model_name=_convert_from_rest_object(obj.model_name) if obj.model_name is not None else None,
+            momentum=_convert_from_rest_object(obj.momentum) if obj.momentum is not None else None,
+            nesterov=_convert_from_rest_object(obj.nesterov) if obj.nesterov is not None else None,
+            number_of_epochs=(
+                _convert_from_rest_object(obj.number_of_epochs) if obj.number_of_epochs is not None else None
+            ),
+            number_of_workers=(
+                _convert_from_rest_object(obj.number_of_workers) if obj.number_of_workers is not None else None
+            ),
+            optimizer=_convert_from_rest_object(obj.optimizer) if obj.optimizer is not None else None,
+            random_seed=_convert_from_rest_object(obj.random_seed) if obj.random_seed is not None else None,
+            step_lr_gamma=_convert_from_rest_object(obj.step_lr_gamma) if obj.step_lr_gamma is not None else None,
+            step_lr_step_size=(
+                _convert_from_rest_object(obj.step_lr_step_size) if obj.step_lr_step_size is not None else None
+            ),
+            training_batch_size=(
+                _convert_from_rest_object(obj.training_batch_size) if obj.training_batch_size is not None else None
+            ),
+            validation_batch_size=(
+                _convert_from_rest_object(obj.validation_batch_size) if obj.validation_batch_size is not None else None
+            ),
+            warmup_cosine_lr_cycles=(
+                _convert_from_rest_object(obj.warmup_cosine_lr_cycles)
+                if obj.warmup_cosine_lr_cycles is not None
+                else None
+            ),
+            warmup_cosine_lr_warmup_epochs=(
+                _convert_from_rest_object(obj.warmup_cosine_lr_warmup_epochs)
+                if obj.warmup_cosine_lr_warmup_epochs is not None
+                else None
+            ),
+            weight_decay=_convert_from_rest_object(obj.weight_decay) if obj.weight_decay is not None else None,
+            training_crop_size=(
+                _convert_from_rest_object(obj.training_crop_size) if obj.training_crop_size is not None else None
+            ),
+            validation_crop_size=(
+                _convert_from_rest_object(obj.validation_crop_size) if obj.validation_crop_size is not None else None
+            ),
+            validation_resize_size=(
+                _convert_from_rest_object(obj.validation_resize_size)
+                if obj.validation_resize_size is not None
+                else None
+            ),
+            weighted_loss=_convert_from_rest_object(obj.weighted_loss) if obj.weighted_loss is not None else None,
+        )
+
+    @classmethod
+    def _from_search_space_object(cls, obj: SearchSpace) -> "ImageClassificationSearchSpace":
+        return cls(
+            ams_gradient=obj.ams_gradient if hasattr(obj, "ams_gradient") else None,
+            beta1=obj.beta1 if hasattr(obj, "beta1") else None,
+            beta2=obj.beta2 if hasattr(obj, "beta2") else None,
+            distributed=obj.distributed if hasattr(obj, "distributed") else None,
+            early_stopping=obj.early_stopping if hasattr(obj, "early_stopping") else None,
+            early_stopping_delay=obj.early_stopping_delay if hasattr(obj, "early_stopping_delay") else None,
+            early_stopping_patience=obj.early_stopping_patience if hasattr(obj, "early_stopping_patience") else None,
+            enable_onnx_normalization=(
+                obj.enable_onnx_normalization if hasattr(obj, "enable_onnx_normalization") else None
+            ),
+            evaluation_frequency=obj.evaluation_frequency if hasattr(obj, "evaluation_frequency") else None,
+            gradient_accumulation_step=(
+                obj.gradient_accumulation_step if hasattr(obj, "gradient_accumulation_step") else None
+            ),
+            layers_to_freeze=obj.layers_to_freeze if hasattr(obj, "layers_to_freeze") else None,
+            learning_rate=obj.learning_rate if hasattr(obj, "learning_rate") else None,
+            learning_rate_scheduler=obj.learning_rate_scheduler if hasattr(obj, "learning_rate_scheduler") else None,
+            model_name=obj.model_name if hasattr(obj, "model_name") else None,
+            momentum=obj.momentum if hasattr(obj, "momentum") else None,
+            nesterov=obj.nesterov if hasattr(obj, "nesterov") else None,
+            number_of_epochs=obj.number_of_epochs if hasattr(obj, "number_of_epochs") else None,
+            number_of_workers=obj.number_of_workers if hasattr(obj, "number_of_workers") else None,
+            optimizer=obj.optimizer if hasattr(obj, "optimizer") else None,
+            random_seed=obj.random_seed if hasattr(obj, "random_seed") else None,
+            step_lr_gamma=obj.step_lr_gamma if hasattr(obj, "step_lr_gamma") else None,
+            step_lr_step_size=obj.step_lr_step_size if hasattr(obj, "step_lr_step_size") else None,
+            training_batch_size=obj.training_batch_size if hasattr(obj, "training_batch_size") else None,
+            validation_batch_size=obj.validation_batch_size if hasattr(obj, "validation_batch_size") else None,
+            warmup_cosine_lr_cycles=obj.warmup_cosine_lr_cycles if hasattr(obj, "warmup_cosine_lr_cycles") else None,
+            warmup_cosine_lr_warmup_epochs=(
+                obj.warmup_cosine_lr_warmup_epochs if hasattr(obj, "warmup_cosine_lr_warmup_epochs") else None
+            ),
+            weight_decay=obj.weight_decay if hasattr(obj, "weight_decay") else None,
+            training_crop_size=obj.training_crop_size if hasattr(obj, "training_crop_size") else None,
+            validation_crop_size=obj.validation_crop_size if hasattr(obj, "validation_crop_size") else None,
+            validation_resize_size=obj.validation_resize_size if hasattr(obj, "validation_resize_size") else None,
+            weighted_loss=obj.weighted_loss if hasattr(obj, "weighted_loss") else None,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ImageClassificationSearchSpace):
+            return NotImplemented
+
+        return (
+            self.ams_gradient == other.ams_gradient
+            and self.beta1 == other.beta1
+            and self.beta2 == other.beta2
+            and self.distributed == other.distributed
+            and self.early_stopping == other.early_stopping
+            and self.early_stopping_delay == other.early_stopping_delay
+            and self.early_stopping_patience == other.early_stopping_patience
+            and self.enable_onnx_normalization == other.enable_onnx_normalization
+            and self.evaluation_frequency == other.evaluation_frequency
+            and self.gradient_accumulation_step == other.gradient_accumulation_step
+            and self.layers_to_freeze == other.layers_to_freeze
+            and self.learning_rate == other.learning_rate
+            and self.learning_rate_scheduler == other.learning_rate_scheduler
+            and self.model_name == other.model_name
+            and self.momentum == other.momentum
+            and self.nesterov == other.nesterov
+            and self.number_of_epochs == other.number_of_epochs
+            and self.number_of_workers == other.number_of_workers
+            and self.optimizer == other.optimizer
+            and self.random_seed == other.random_seed
+            and self.step_lr_gamma == other.step_lr_gamma
+            and self.step_lr_step_size == other.step_lr_step_size
+            and self.training_batch_size == other.training_batch_size
+            and self.validation_batch_size == other.validation_batch_size
+            and self.warmup_cosine_lr_cycles == other.warmup_cosine_lr_cycles
+            and self.warmup_cosine_lr_warmup_epochs == other.warmup_cosine_lr_warmup_epochs
+            and self.weight_decay == other.weight_decay
+            and self.training_crop_size == other.training_crop_size
+            and self.validation_crop_size == other.validation_crop_size
+            and self.validation_resize_size == other.validation_resize_size
+            and self.weighted_loss == other.weighted_loss
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_instance_segmentation_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_instance_segmentation_job.py
new file mode 100644
index 00000000..c97d3c11
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_instance_segmentation_job.py
@@ -0,0 +1,249 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    ImageInstanceSegmentation as RestImageInstanceSegmentation,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import InstanceSegmentationPrimaryMetrics, JobBase, TaskType
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.image.automl_image_object_detection_base import AutoMLImageObjectDetectionBase
+from azure.ai.ml.entities._job.automl.image.image_limit_settings import ImageLimitSettings
+from azure.ai.ml.entities._job.automl.image.image_model_settings import ImageModelSettingsObjectDetection
+from azure.ai.ml.entities._job.automl.image.image_sweep_settings import ImageSweepSettings
+from azure.ai.ml.entities._util import load_from_dict
+
+
+class ImageInstanceSegmentationJob(AutoMLImageObjectDetectionBase):
+    """Configuration for AutoML Image Instance Segmentation job.
+
+    :keyword primary_metric: The primary metric to use for optimization.
+    :paramtype primary_metric: Optional[str, ~azure.ai.ml.automl.InstanceSegmentationPrimaryMetrics]
+    :keyword kwargs: Job-specific arguments.
+    :paramtype kwargs: Dict[str, Any]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+            :start-after: [START automl.automl_image_job.image_instance_segmentation_job]
+            :end-before: [END automl.automl_image_job.image_instance_segmentation_job]
+            :language: python
+            :dedent: 8
+            :caption: creating an automl image instance segmentation job
+    """
+
+    _DEFAULT_PRIMARY_METRIC = InstanceSegmentationPrimaryMetrics.MEAN_AVERAGE_PRECISION
+
+    def __init__(
+        self,
+        *,
+        primary_metric: Optional[Union[str, InstanceSegmentationPrimaryMetrics]] = None,
+        **kwargs: Any,
+    ) -> None:
+        # Extract any super class init settings
+        limits = kwargs.pop("limits", None)
+        sweep = kwargs.pop("sweep", None)
+        training_parameters = kwargs.pop("training_parameters", None)
+        search_space = kwargs.pop("search_space", None)
+
+        super().__init__(
+            task_type=TaskType.IMAGE_INSTANCE_SEGMENTATION,
+            limits=limits,
+            sweep=sweep,
+            training_parameters=training_parameters,
+            search_space=search_space,
+            **kwargs,
+        )
+        self.primary_metric = primary_metric or ImageInstanceSegmentationJob._DEFAULT_PRIMARY_METRIC
+
+    @property
+    def primary_metric(self) -> Union[str, InstanceSegmentationPrimaryMetrics]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, InstanceSegmentationPrimaryMetrics]) -> None:
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+        self._primary_metric = (
+            ImageInstanceSegmentationJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else InstanceSegmentationPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        image_instance_segmentation_task = RestImageInstanceSegmentation(
+            target_column_name=self.target_column_name,
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            validation_data_size=self.validation_data_size,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            sweep_settings=self._sweep._to_rest_object() if self._sweep else None,
+            model_settings=self._training_parameters._to_rest_object() if self._training_parameters else None,
+            search_space=(
+                [entry._to_rest_object() for entry in self._search_space if entry is not None]
+                if self._search_space is not None
+                else None
+            ),
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        # resolve data inputs in rest obj
+        self._resolve_data_inputs(image_instance_segmentation_task)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=image_instance_segmentation_task,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "ImageInstanceSegmentationJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestImageInstanceSegmentation = properties.task_details
+
+        job_args_dict = {
+            "id": obj.id,
+            "name": obj.name,
+            "description": properties.description,
+            "tags": properties.tags,
+            "properties": properties.properties,
+            "experiment_name": properties.experiment_name,
+            "services": properties.services,
+            "status": properties.status,
+            "creation_context": obj.system_data,
+            "display_name": properties.display_name,
+            "compute": properties.compute_id,
+            "outputs": from_rest_data_outputs(properties.outputs),
+            "resources": properties.resources,
+            "identity": (
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            "queue_settings": properties.queue_settings,
+        }
+
+        image_instance_segmentation_job = cls(
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            validation_data_size=task_details.validation_data_size,
+            limits=(
+                ImageLimitSettings._from_rest_object(task_details.limit_settings)
+                if task_details.limit_settings
+                else None
+            ),
+            sweep=(
+                ImageSweepSettings._from_rest_object(task_details.sweep_settings)
+                if task_details.sweep_settings
+                else None
+            ),
+            training_parameters=(
+                ImageModelSettingsObjectDetection._from_rest_object(task_details.model_settings)
+                if task_details.model_settings
+                else None
+            ),
+            search_space=cls._get_search_space_from_str(task_details.search_space),
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            **job_args_dict,
+        )
+
+        image_instance_segmentation_job._restore_data_inputs()
+
+        return image_instance_segmentation_job
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "ImageInstanceSegmentationJob":
+        from azure.ai.ml._schema.automl.image_vertical.image_object_detection import ImageInstanceSegmentationSchema
+        from azure.ai.ml._schema.pipeline.automl_node import ImageInstanceSegmentationNodeSchema
+
+        inside_pipeline = kwargs.pop("inside_pipeline", False)
+        if inside_pipeline:
+            if context.get("inside_pipeline", None) is None:
+                context["inside_pipeline"] = True
+            loaded_data = load_from_dict(
+                ImageInstanceSegmentationNodeSchema,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        else:
+            loaded_data = load_from_dict(
+                ImageInstanceSegmentationSchema,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "ImageInstanceSegmentationJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        data_settings = {
+            "training_data": loaded_data.pop("training_data"),
+            "target_column_name": loaded_data.pop("target_column_name"),
+            "validation_data": loaded_data.pop("validation_data", None),
+            "validation_data_size": loaded_data.pop("validation_data_size", None),
+        }
+        job = ImageInstanceSegmentationJob(**loaded_data)
+        job.set_data(**data_settings)
+        return job
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.image_vertical.image_object_detection import ImageInstanceSegmentationSchema
+        from azure.ai.ml._schema.pipeline.automl_node import ImageInstanceSegmentationNodeSchema
+
+        schema_dict: dict = {}
+        if inside_pipeline:
+            schema_dict = ImageInstanceSegmentationNodeSchema(
+                context={BASE_PATH_CONTEXT_KEY: "./", "inside_pipeline": True}
+            ).dump(self)
+        else:
+            schema_dict = ImageInstanceSegmentationSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+
+        return schema_dict
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ImageInstanceSegmentationJob):
+            return NotImplemented
+
+        if not super().__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_limit_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_limit_settings.py
new file mode 100644
index 00000000..12ec8b57
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_limit_settings.py
@@ -0,0 +1,117 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ImageLimitSettings as RestImageLimitSettings
+from azure.ai.ml._utils.utils import from_iso_duration_format_mins, to_iso_duration_format_mins
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class ImageLimitSettings(RestTranslatableMixin):
+    r"""Limit settings for AutoML Image Verticals.
+
+    ImageLimitSettings is a class that contains the following parameters:  max_concurrent_trials, max_trials, and \
+    timeout_minutes.
+
+    This is an optional configuration method to configure limits parameters such as timeouts etc.
+
+        .. note::
+
+            The number of concurrent runs is gated on the resources available in the specified compute target.
+            Ensure that the compute target has the available resources for the desired concurrency.
+
+    :keyword max_concurrent_trials: Maximum number of concurrent AutoML iterations, defaults to None.
+    :paramtype  max_concurrent_trials: typing.Optional[int]
+    :keyword max_trials: Represents the maximum number of trials (children jobs).
+    :paramtype  max_trials: typing.Optional[int]
+    :keyword timeout_minutes: AutoML job timeout. Defaults to None
+    :paramtype  timeout_minutes: typing.Optional[int]
+    :raises ValueError: If max_concurrent_trials is not None and is not a positive integer.
+    :raises ValueError: If max_trials is not None and is not a positive integer.
+    :raises ValueError: If timeout_minutes is not None and is not a positive integer.
+    :return: ImageLimitSettings object.
+    :rtype: ImageLimitSettings
+
+    .. tip::
+        It's a good practice to match max_concurrent_trials count with the number of nodes in the cluster.
+        For example, if you have a cluster with 4 nodes, set max_concurrent_trials to 4.
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+                :start-after: [START automl.automl_image_job.image_limit_settings]
+                :end-before: [END automl.automl_image_job.image_limit_settings]
+                :language: python
+                :dedent: 8
+                :caption: Defining the limit settings for an automl image job.
+    """
+
+    def __init__(
+        self,
+        *,
+        max_concurrent_trials: Optional[int] = None,
+        max_trials: Optional[int] = None,
+        timeout_minutes: Optional[int] = None,
+    ) -> None:
+        self.max_concurrent_trials = max_concurrent_trials
+        self.max_trials = max_trials
+        self.timeout_minutes = timeout_minutes
+
+    def _to_rest_object(self) -> RestImageLimitSettings:
+        """Convert ImageLimitSettings objects to a rest object.
+
+        :return: A rest object of ImageLimitSettings objects.
+        :rtype: RestImageLimitSettings
+        """
+        return RestImageLimitSettings(
+            max_concurrent_trials=self.max_concurrent_trials,
+            max_trials=self.max_trials,
+            timeout=to_iso_duration_format_mins(self.timeout_minutes),
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestImageLimitSettings) -> "ImageLimitSettings":
+        """Convert the rest object to a dict containing items to init the ImageLimitSettings objects.
+
+        :param obj: Limit settings for the AutoML job in Rest format.
+        :type obj: RestImageLimitSettings
+        :return: Limit settings for an AutoML Image Vertical.
+        :rtype: ImageLimitSettings
+        """
+        return cls(
+            max_concurrent_trials=obj.max_concurrent_trials,
+            max_trials=obj.max_trials,
+            timeout_minutes=from_iso_duration_format_mins(obj.timeout),
+        )
+
+    def __eq__(self, other: object) -> bool:
+        """Check equality between two ImageLimitSettings objects.
+
+        This method check instances equality and returns True if both of
+            the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, ImageLimitSettings):
+            return NotImplemented
+
+        return (
+            self.max_concurrent_trials == other.max_concurrent_trials
+            and self.max_trials == other.max_trials
+            and self.timeout_minutes == other.timeout_minutes
+        )
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two ImageLimitSettings objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_model_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_model_settings.py
new file mode 100644
index 00000000..890f987a
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_model_settings.py
@@ -0,0 +1,876 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Any, Optional
+
+# pylint: disable=R0902,too-many-locals
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    ImageModelSettingsClassification as RestImageModelSettingsClassification,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    ImageModelSettingsObjectDetection as RestImageModelSettingsObjectDetection,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    LearningRateScheduler,
+    LogTrainingMetrics,
+    LogValidationLoss,
+    ModelSize,
+    StochasticOptimizer,
+    ValidationMetricType,
+)
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class ImageModelDistributionSettings(RestTranslatableMixin):
+    """Model settings for all AutoML Image Verticals.
+    Please do not instantiate directly. Use the child classes instead.
+
+    :param advanced_settings: Settings for advanced scenarios.
+    :type advanced_settings: str
+    :param ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :type ams_gradient: bool
+    :param beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :type beta1: float
+    :param beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :type beta2: float
+    :param checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :type checkpoint_frequency: int
+    :param checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :type checkpoint_run_id: str
+    :param distributed: Whether to use distributed training.
+    :type distributed: bool
+    :param early_stopping: Enable early stopping logic during training.
+    :type early_stopping: bool
+    :param early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :type early_stopping_delay: int
+    :param early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :type early_stopping_patience: int
+    :param enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :type enable_onnx_normalization: bool
+    :param evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :type evaluation_frequency: int
+    :param gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :type gradient_accumulation_step: int
+    :param layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :type layers_to_freeze: int
+    :param learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :type learning_rate: float
+    :param learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Possible values include: "None", "WarmupCosine", "Step".
+    :type learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :param model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :type model_name: str
+    :param momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :type momentum: float
+    :param nesterov: Enable nesterov when optimizer is 'sgd'.
+    :type nesterov: bool
+    :param number_of_epochs: Number of training epochs. Must be a positive integer.
+    :type number_of_epochs: int
+    :param number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :type number_of_workers: int
+    :param optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+    :type optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :param random_seed: Random seed to be used when using deterministic training.
+    :type random_seed: int
+    :param step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :type step_lr_gamma: float
+    :param step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :type step_lr_step_size: int
+    :param training_batch_size: Training batch size. Must be a positive integer.
+    :type training_batch_size: int
+    :param validation_batch_size: Validation batch size. Must be a positive integer.
+    :type validation_batch_size: int
+    :param warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :type warmup_cosine_lr_cycles: float
+    :param warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :type warmup_cosine_lr_warmup_epochs: int
+    :param weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :type weight_decay: float
+    """
+
+    def __init__(
+        self,
+        *,
+        advanced_settings: Optional[str] = None,
+        ams_gradient: Optional[bool] = None,
+        beta1: Optional[float] = None,
+        beta2: Optional[float] = None,
+        checkpoint_frequency: Optional[int] = None,
+        checkpoint_run_id: Optional[str] = None,
+        distributed: Optional[bool] = None,
+        early_stopping: Optional[bool] = None,
+        early_stopping_delay: Optional[int] = None,
+        early_stopping_patience: Optional[int] = None,
+        enable_onnx_normalization: Optional[bool] = None,
+        evaluation_frequency: Optional[int] = None,
+        gradient_accumulation_step: Optional[int] = None,
+        layers_to_freeze: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[LearningRateScheduler] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[float] = None,
+        nesterov: Optional[bool] = None,
+        number_of_epochs: Optional[int] = None,
+        number_of_workers: Optional[int] = None,
+        optimizer: Optional[StochasticOptimizer] = None,
+        random_seed: Optional[int] = None,
+        step_lr_gamma: Optional[float] = None,
+        step_lr_step_size: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_cosine_lr_cycles: Optional[float] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[int] = None,
+        weight_decay: Optional[float] = None,
+    ):
+        self.advanced_settings = advanced_settings
+        self.ams_gradient = ams_gradient
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.checkpoint_frequency = checkpoint_frequency
+        self.checkpoint_run_id = checkpoint_run_id
+        self.distributed = distributed
+        self.early_stopping = early_stopping
+        self.early_stopping_delay = early_stopping_delay
+        self.early_stopping_patience = early_stopping_patience
+        self.enable_onnx_normalization = enable_onnx_normalization
+        self.evaluation_frequency = evaluation_frequency
+        self.gradient_accumulation_step = gradient_accumulation_step
+        self.layers_to_freeze = layers_to_freeze
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.momentum = momentum
+        self.nesterov = nesterov
+        self.number_of_epochs = number_of_epochs
+        self.number_of_workers = number_of_workers
+        self.optimizer = optimizer
+        self.random_seed = random_seed
+        self.step_lr_gamma = step_lr_gamma
+        self.step_lr_step_size = step_lr_step_size
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_cosine_lr_cycles = warmup_cosine_lr_cycles
+        self.warmup_cosine_lr_warmup_epochs = warmup_cosine_lr_warmup_epochs
+        self.weight_decay = weight_decay
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ImageModelDistributionSettings):
+            return NotImplemented
+
+        return (
+            self.advanced_settings == other.advanced_settings
+            and self.ams_gradient == other.ams_gradient
+            and self.beta1 == other.beta1
+            and self.beta2 == other.beta2
+            and self.checkpoint_frequency == other.checkpoint_frequency
+            and self.checkpoint_run_id == other.checkpoint_run_id
+            and self.distributed == other.distributed
+            and self.early_stopping == other.early_stopping
+            and self.early_stopping_delay == other.early_stopping_delay
+            and self.early_stopping_patience == other.early_stopping_patience
+            and self.enable_onnx_normalization == other.enable_onnx_normalization
+            and self.evaluation_frequency == other.evaluation_frequency
+            and self.gradient_accumulation_step == other.gradient_accumulation_step
+            and self.layers_to_freeze == other.layers_to_freeze
+            and self.learning_rate == other.learning_rate
+            and self.learning_rate_scheduler == other.learning_rate_scheduler
+            and self.model_name == other.model_name
+            and self.momentum == other.momentum
+            and self.nesterov == other.nesterov
+            and self.number_of_epochs == other.number_of_epochs
+            and self.number_of_workers == other.number_of_workers
+            and self.optimizer == other.optimizer
+            and self.random_seed == other.random_seed
+            and self.step_lr_gamma == other.step_lr_gamma
+            and self.step_lr_step_size == other.step_lr_step_size
+            and self.training_batch_size == other.training_batch_size
+            and self.validation_batch_size == other.validation_batch_size
+            and self.warmup_cosine_lr_cycles == other.warmup_cosine_lr_cycles
+            and self.warmup_cosine_lr_warmup_epochs == other.warmup_cosine_lr_warmup_epochs
+            and self.weight_decay == other.weight_decay
+        )
+
+
+class ImageModelSettingsClassification(ImageModelDistributionSettings):
+    """Model settings for AutoML Image Classification tasks.
+
+    :param advanced_settings: Settings for advanced scenarios.
+    :type advanced_settings: str
+    :param ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :type ams_gradient: bool
+    :param beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :type beta1: float
+    :param beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :type beta2: float
+    :param checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :type checkpoint_frequency: int
+    :param checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :type checkpoint_run_id: str
+    :param distributed: Whether to use distributed training.
+    :type distributed: bool
+    :param early_stopping: Enable early stopping logic during training.
+    :type early_stopping: bool
+    :param early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :type early_stopping_delay: int
+    :param early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :type early_stopping_patience: int
+    :param enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :type enable_onnx_normalization: bool
+    :param evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :type evaluation_frequency: int
+    :param gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :type gradient_accumulation_step: int
+    :param layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :type layers_to_freeze: int
+    :param learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :type learning_rate: float
+    :param learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Possible values include: "None", "WarmupCosine", "Step".
+    :type learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :param model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :type model_name: str
+    :param momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :type momentum: float
+    :param nesterov: Enable nesterov when optimizer is 'sgd'.
+    :type nesterov: bool
+    :param number_of_epochs: Number of training epochs. Must be a positive integer.
+    :type number_of_epochs: int
+    :param number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :type number_of_workers: int
+    :param optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+    :type optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :param random_seed: Random seed to be used when using deterministic training.
+    :type random_seed: int
+    :param step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :type step_lr_gamma: float
+    :param step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :type step_lr_step_size: int
+    :param training_batch_size: Training batch size. Must be a positive integer.
+    :type training_batch_size: int
+    :param validation_batch_size: Validation batch size. Must be a positive integer.
+    :type validation_batch_size: int
+    :param warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :type warmup_cosine_lr_cycles: float
+    :param warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :type warmup_cosine_lr_warmup_epochs: int
+    :param weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :type weight_decay: float
+    :param training_crop_size: Image crop size that is input to the neural network for the training
+     dataset. Must be a positive integer.
+    :type training_crop_size: int
+    :param validation_crop_size: Image crop size that is input to the neural network for the
+     validation dataset. Must be a positive integer.
+    :type validation_crop_size: int
+    :param validation_resize_size: Image size to which to resize before cropping for validation
+     dataset. Must be a positive integer.
+    :type validation_resize_size: int
+    :param weighted_loss: Weighted loss. The accepted values are 0 for no weighted loss.
+     1 for weighted loss with sqrt.(class_weights). 2 for weighted loss with class_weights. Must be
+     0 or 1 or 2.
+    :type weighted_loss: int
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+            :start-after: [START automl.automl_image_job.image_classification_model_settings]
+            :end-before: [END automl.automl_image_job.image_classification_model_settings]
+            :language: python
+            :dedent: 8
+            :caption: Defining the automl image classification model settings.
+    """
+
+    def __init__(
+        self,
+        *,
+        advanced_settings: Optional[str] = None,
+        ams_gradient: Optional[bool] = None,
+        beta1: Optional[float] = None,
+        beta2: Optional[float] = None,
+        checkpoint_frequency: Optional[int] = None,
+        checkpoint_run_id: Optional[str] = None,
+        distributed: Optional[bool] = None,
+        early_stopping: Optional[bool] = None,
+        early_stopping_delay: Optional[int] = None,
+        early_stopping_patience: Optional[int] = None,
+        enable_onnx_normalization: Optional[bool] = None,
+        evaluation_frequency: Optional[int] = None,
+        gradient_accumulation_step: Optional[int] = None,
+        layers_to_freeze: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[LearningRateScheduler] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[float] = None,
+        nesterov: Optional[bool] = None,
+        number_of_epochs: Optional[int] = None,
+        number_of_workers: Optional[int] = None,
+        optimizer: Optional[StochasticOptimizer] = None,
+        random_seed: Optional[int] = None,
+        step_lr_gamma: Optional[float] = None,
+        step_lr_step_size: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_cosine_lr_cycles: Optional[float] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[int] = None,
+        weight_decay: Optional[float] = None,
+        training_crop_size: Optional[int] = None,
+        validation_crop_size: Optional[int] = None,
+        validation_resize_size: Optional[int] = None,
+        weighted_loss: Optional[int] = None,
+        **kwargs: Any,
+    ):
+        super(ImageModelSettingsClassification, self).__init__(
+            advanced_settings=advanced_settings,
+            ams_gradient=ams_gradient,
+            beta1=beta1,
+            beta2=beta2,
+            checkpoint_frequency=checkpoint_frequency,
+            checkpoint_run_id=checkpoint_run_id,
+            distributed=distributed,
+            early_stopping=early_stopping,
+            early_stopping_delay=early_stopping_delay,
+            early_stopping_patience=early_stopping_patience,
+            enable_onnx_normalization=enable_onnx_normalization,
+            evaluation_frequency=evaluation_frequency,
+            gradient_accumulation_step=gradient_accumulation_step,
+            layers_to_freeze=layers_to_freeze,
+            learning_rate=learning_rate,
+            learning_rate_scheduler=learning_rate_scheduler,
+            model_name=model_name,
+            momentum=momentum,
+            nesterov=nesterov,
+            number_of_epochs=number_of_epochs,
+            number_of_workers=number_of_workers,
+            optimizer=optimizer,
+            random_seed=random_seed,
+            step_lr_gamma=step_lr_gamma,
+            step_lr_step_size=step_lr_step_size,
+            training_batch_size=training_batch_size,
+            validation_batch_size=validation_batch_size,
+            warmup_cosine_lr_cycles=warmup_cosine_lr_cycles,
+            warmup_cosine_lr_warmup_epochs=warmup_cosine_lr_warmup_epochs,
+            weight_decay=weight_decay,
+            **kwargs,
+        )
+        self.training_crop_size = training_crop_size
+        self.validation_crop_size = validation_crop_size
+        self.validation_resize_size = validation_resize_size
+        self.weighted_loss = weighted_loss
+
+    def _to_rest_object(self) -> RestImageModelSettingsClassification:
+        return RestImageModelSettingsClassification(
+            advanced_settings=self.advanced_settings,
+            ams_gradient=self.ams_gradient,
+            beta1=self.beta1,
+            beta2=self.beta2,
+            checkpoint_frequency=self.checkpoint_frequency,
+            checkpoint_run_id=self.checkpoint_run_id,
+            distributed=self.distributed,
+            early_stopping=self.early_stopping,
+            early_stopping_delay=self.early_stopping_delay,
+            early_stopping_patience=self.early_stopping_patience,
+            enable_onnx_normalization=self.enable_onnx_normalization,
+            evaluation_frequency=self.evaluation_frequency,
+            gradient_accumulation_step=self.gradient_accumulation_step,
+            layers_to_freeze=self.layers_to_freeze,
+            learning_rate=self.learning_rate,
+            learning_rate_scheduler=self.learning_rate_scheduler,
+            model_name=self.model_name,
+            momentum=self.momentum,
+            nesterov=self.nesterov,
+            number_of_epochs=self.number_of_epochs,
+            number_of_workers=self.number_of_workers,
+            optimizer=self.optimizer,
+            random_seed=self.random_seed,
+            step_lr_gamma=self.step_lr_gamma,
+            step_lr_step_size=self.step_lr_step_size,
+            training_batch_size=self.training_batch_size,
+            validation_batch_size=self.validation_batch_size,
+            warmup_cosine_lr_cycles=self.warmup_cosine_lr_cycles,
+            warmup_cosine_lr_warmup_epochs=self.warmup_cosine_lr_warmup_epochs,
+            weight_decay=self.weight_decay,
+            training_crop_size=self.training_crop_size,
+            validation_crop_size=self.validation_crop_size,
+            validation_resize_size=self.validation_resize_size,
+            weighted_loss=self.weighted_loss,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestImageModelSettingsClassification) -> "ImageModelSettingsClassification":
+        return cls(
+            advanced_settings=obj.advanced_settings,
+            ams_gradient=obj.ams_gradient,
+            beta1=obj.beta1,
+            beta2=obj.beta2,
+            checkpoint_frequency=obj.checkpoint_frequency,
+            checkpoint_run_id=obj.checkpoint_run_id,
+            distributed=obj.distributed,
+            early_stopping=obj.early_stopping,
+            early_stopping_delay=obj.early_stopping_delay,
+            early_stopping_patience=obj.early_stopping_patience,
+            enable_onnx_normalization=obj.enable_onnx_normalization,
+            evaluation_frequency=obj.evaluation_frequency,
+            gradient_accumulation_step=obj.gradient_accumulation_step,
+            layers_to_freeze=obj.layers_to_freeze,
+            learning_rate=obj.learning_rate,
+            learning_rate_scheduler=obj.learning_rate_scheduler,
+            model_name=obj.model_name,
+            momentum=obj.momentum,
+            nesterov=obj.nesterov,
+            number_of_epochs=obj.number_of_epochs,
+            number_of_workers=obj.number_of_workers,
+            optimizer=obj.optimizer,
+            random_seed=obj.random_seed,
+            step_lr_gamma=obj.step_lr_gamma,
+            step_lr_step_size=obj.step_lr_step_size,
+            training_batch_size=obj.training_batch_size,
+            validation_batch_size=obj.validation_batch_size,
+            warmup_cosine_lr_cycles=obj.warmup_cosine_lr_cycles,
+            warmup_cosine_lr_warmup_epochs=obj.warmup_cosine_lr_warmup_epochs,
+            weight_decay=obj.weight_decay,
+            training_crop_size=obj.training_crop_size,
+            validation_crop_size=obj.validation_crop_size,
+            validation_resize_size=obj.validation_resize_size,
+            weighted_loss=obj.weighted_loss,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ImageModelSettingsClassification):
+            return NotImplemented
+
+        return (
+            super().__eq__(other)
+            and self.training_crop_size == other.training_crop_size
+            and self.validation_crop_size == other.validation_crop_size
+            and self.validation_resize_size == other.validation_resize_size
+            and self.weighted_loss == other.weighted_loss
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
+
+
+class ImageModelSettingsObjectDetection(ImageModelDistributionSettings):
+    """Model settings for AutoML Image Object Detection Task.
+
+    :param advanced_settings: Settings for advanced scenarios.
+    :type advanced_settings: str
+    :param ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :type ams_gradient: bool
+    :param beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :type beta1: float
+    :param beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the range
+     [0, 1].
+    :type beta2: float
+    :param checkpoint_frequency: Frequency to store model checkpoints. Must be a positive integer.
+    :type checkpoint_frequency: int
+    :param checkpoint_run_id: The id of a previous run that has a pretrained checkpoint for
+     incremental training.
+    :type checkpoint_run_id: str
+    :param distributed: Whether to use distributed training.
+    :type distributed: bool
+    :param early_stopping: Enable early stopping logic during training.
+    :type early_stopping: bool
+    :param early_stopping_delay: Minimum number of epochs or validation evaluations to wait before
+     primary metric improvement
+     is tracked for early stopping. Must be a positive integer.
+    :type early_stopping_delay: int
+    :param early_stopping_patience: Minimum number of epochs or validation evaluations with no
+     primary metric improvement before
+     the run is stopped. Must be a positive integer.
+    :type early_stopping_patience: int
+    :param enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :type enable_onnx_normalization: bool
+    :param evaluation_frequency: Frequency to evaluate validation dataset to get metric scores. Must
+     be a positive integer.
+    :type evaluation_frequency: int
+    :param gradient_accumulation_step: Gradient accumulation means running a configured number of
+     "GradAccumulationStep" steps without
+     updating the model weights while accumulating the gradients of those steps, and then using
+     the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :type gradient_accumulation_step: int
+    :param layers_to_freeze: Number of layers to freeze for the model. Must be a positive integer.
+     For instance, passing 2 as value for 'seresnext' means
+     freezing layer0 and layer1. For a full list of models supported and details on layer freeze,
+     please
+     see: https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :type layers_to_freeze: int
+    :param learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+    :type learning_rate: float
+    :param learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+     'step'. Possible values include: "None", "WarmupCosine", "Step".
+    :type learning_rate_scheduler: str or
+     ~azure.mgmt.machinelearningservices.models.LearningRateScheduler
+    :param model_name: Name of the model to use for training.
+     For more information on the available models please visit the official documentation:
+     https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :type model_name: str
+    :param momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0, 1].
+    :type momentum: float
+    :param nesterov: Enable nesterov when optimizer is 'sgd'.
+    :type nesterov: bool
+    :param number_of_epochs: Number of training epochs. Must be a positive integer.
+    :type number_of_epochs: int
+    :param number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :type number_of_workers: int
+    :param optimizer: Type of optimizer. Possible values include: "None", "Sgd", "Adam", "Adamw".
+    :type optimizer: str or ~azure.mgmt.machinelearningservices.models.StochasticOptimizer
+    :param random_seed: Random seed to be used when using deterministic training.
+    :type random_seed: int
+    :param step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float in
+     the range [0, 1].
+    :type step_lr_gamma: float
+    :param step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be a
+     positive integer.
+    :type step_lr_step_size: int
+    :param training_batch_size: Training batch size. Must be a positive integer.
+    :type training_batch_size: int
+    :param validation_batch_size: Validation batch size. Must be a positive integer.
+    :type validation_batch_size: int
+    :param warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+     'warmup_cosine'. Must be a float in the range [0, 1].
+    :type warmup_cosine_lr_cycles: float
+    :param warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+     'warmup_cosine'. Must be a positive integer.
+    :type warmup_cosine_lr_warmup_epochs: int
+    :param weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must be
+     a float in the range[0, 1].
+    :type weight_decay: float
+    :param box_detections_per_image: Maximum number of detections per image, for all classes. Must
+     be a positive integer.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :type box_detections_per_image: int
+    :param box_score_threshold: During inference, only return proposals with a classification score
+     greater than
+     BoxScoreThreshold. Must be a float in the range[0, 1].
+    :type box_score_threshold: float
+    :param image_size: Image size for train and validation. Must be a positive integer.
+     Note: The training run may get into CUDA OOM if the size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :type image_size: int
+    :param max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :type max_size: int
+    :param min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+     Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :type min_size: int
+    :param model_size: Model size. Must be 'small', 'medium', 'large'.
+     Note: training run may get into CUDA OOM if the model size is too big.
+     Note: This settings is only supported for the 'yolov5' algorithm. Possible values include:
+     "None", "Small", "Medium", "Large", "ExtraLarge".
+    :type model_size: str or ~azure.mgmt.machinelearningservices.models.ModelSize
+    :param multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+     Note: training run may get into CUDA OOM if no sufficient GPU memory.
+     Note: This settings is only supported for the 'yolov5' algorithm.
+    :type multi_scale: bool
+    :param nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be a
+     float in the range [0, 1].
+    :type nms_iou_threshold: float
+    :param tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must not
+     be
+     None to enable small object detection logic. A string containing two integers in mxn format.
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :type tile_grid_size: str
+    :param tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be float
+     in the range [0, 1).
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :type tile_overlap_ratio: float
+    :param tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+     predictions from tiles and image.
+     Used in validation/ inference. Must be float in the range [0, 1].
+     Note: This settings is not supported for the 'yolov5' algorithm.
+    :type tile_predictions_nms_threshold: float
+    :param validation_iou_threshold: IOU threshold to use when computing validation metric. Must be
+     float in the range [0, 1].
+    :type validation_iou_threshold: float
+    :param validation_metric_type: Metric computation method to use for validation metrics. Possible
+     values include: "None", "Coco", "Voc", "CocoVoc".
+    :type validation_metric_type: str or
+     ~azure.mgmt.machinelearningservices.models.ValidationMetricType
+    :param log_training_metrics: indicates whether or not to log training metrics
+    :type log_training_metrics: str or
+     ~azure.mgmt.machinelearningservices.models.LogTrainingMetrics
+    :param log_validation_loss: indicates whether or not to log validation loss
+    :type log_validation_loss: str or
+     ~azure.mgmt.machinelearningservices.models.LogValidationLoss
+
+    .. literalinclude:: ../samples/ml_samples_automl_image.py
+            :start-after: [START automl.automl_image_job.image_object_detection_model_settings]
+            :end-before: [END automl.automl_image_job.image_object_detection_model_settings]
+            :language: python
+            :dedent: 8
+            :caption: Defining the automl image object detection or instance segmentation model settings.
+    """
+
+    def __init__(
+        self,
+        *,
+        advanced_settings: Optional[str] = None,
+        ams_gradient: Optional[bool] = None,
+        beta1: Optional[float] = None,
+        beta2: Optional[float] = None,
+        checkpoint_frequency: Optional[int] = None,
+        checkpoint_run_id: Optional[str] = None,
+        distributed: Optional[bool] = None,
+        early_stopping: Optional[bool] = None,
+        early_stopping_delay: Optional[int] = None,
+        early_stopping_patience: Optional[int] = None,
+        enable_onnx_normalization: Optional[bool] = None,
+        evaluation_frequency: Optional[int] = None,
+        gradient_accumulation_step: Optional[int] = None,
+        layers_to_freeze: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[LearningRateScheduler] = None,
+        model_name: Optional[str] = None,
+        momentum: Optional[float] = None,
+        nesterov: Optional[bool] = None,
+        number_of_epochs: Optional[int] = None,
+        number_of_workers: Optional[int] = None,
+        optimizer: Optional[StochasticOptimizer] = None,
+        random_seed: Optional[int] = None,
+        step_lr_gamma: Optional[float] = None,
+        step_lr_step_size: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_cosine_lr_cycles: Optional[float] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[int] = None,
+        weight_decay: Optional[float] = None,
+        box_detections_per_image: Optional[int] = None,
+        box_score_threshold: Optional[float] = None,
+        image_size: Optional[int] = None,
+        max_size: Optional[int] = None,
+        min_size: Optional[int] = None,
+        model_size: Optional[ModelSize] = None,
+        multi_scale: Optional[bool] = None,
+        nms_iou_threshold: Optional[float] = None,
+        tile_grid_size: Optional[str] = None,
+        tile_overlap_ratio: Optional[float] = None,
+        tile_predictions_nms_threshold: Optional[float] = None,
+        validation_iou_threshold: Optional[float] = None,
+        validation_metric_type: Optional[ValidationMetricType] = None,
+        log_training_metrics: Optional[LogTrainingMetrics] = None,
+        log_validation_loss: Optional[LogValidationLoss] = None,
+        **kwargs: Any,
+    ):
+        super(ImageModelSettingsObjectDetection, self).__init__(
+            advanced_settings=advanced_settings,
+            ams_gradient=ams_gradient,
+            beta1=beta1,
+            beta2=beta2,
+            checkpoint_frequency=checkpoint_frequency,
+            checkpoint_run_id=checkpoint_run_id,
+            distributed=distributed,
+            early_stopping=early_stopping,
+            early_stopping_delay=early_stopping_delay,
+            early_stopping_patience=early_stopping_patience,
+            enable_onnx_normalization=enable_onnx_normalization,
+            evaluation_frequency=evaluation_frequency,
+            gradient_accumulation_step=gradient_accumulation_step,
+            layers_to_freeze=layers_to_freeze,
+            learning_rate=learning_rate,
+            learning_rate_scheduler=learning_rate_scheduler,
+            model_name=model_name,
+            momentum=momentum,
+            nesterov=nesterov,
+            number_of_epochs=number_of_epochs,
+            number_of_workers=number_of_workers,
+            optimizer=optimizer,
+            random_seed=random_seed,
+            step_lr_gamma=step_lr_gamma,
+            step_lr_step_size=step_lr_step_size,
+            training_batch_size=training_batch_size,
+            validation_batch_size=validation_batch_size,
+            warmup_cosine_lr_cycles=warmup_cosine_lr_cycles,
+            warmup_cosine_lr_warmup_epochs=warmup_cosine_lr_warmup_epochs,
+            weight_decay=weight_decay,
+            **kwargs,
+        )
+        self.box_detections_per_image = box_detections_per_image
+        self.box_score_threshold = box_score_threshold
+        self.image_size = image_size
+        self.max_size = max_size
+        self.min_size = min_size
+        self.model_size = model_size
+        self.multi_scale = multi_scale
+        self.nms_iou_threshold = nms_iou_threshold
+        self.tile_grid_size = tile_grid_size
+        self.tile_overlap_ratio = tile_overlap_ratio
+        self.tile_predictions_nms_threshold = tile_predictions_nms_threshold
+        self.validation_iou_threshold = validation_iou_threshold
+        self.validation_metric_type = validation_metric_type
+        self.log_training_metrics = log_training_metrics
+        self.log_validation_loss = log_validation_loss
+
+    def _to_rest_object(self) -> RestImageModelSettingsObjectDetection:
+        return RestImageModelSettingsObjectDetection(
+            advanced_settings=self.advanced_settings,
+            ams_gradient=self.ams_gradient,
+            beta1=self.beta1,
+            beta2=self.beta2,
+            checkpoint_frequency=self.checkpoint_frequency,
+            checkpoint_run_id=self.checkpoint_run_id,
+            distributed=self.distributed,
+            early_stopping=self.early_stopping,
+            early_stopping_delay=self.early_stopping_delay,
+            early_stopping_patience=self.early_stopping_patience,
+            enable_onnx_normalization=self.enable_onnx_normalization,
+            evaluation_frequency=self.evaluation_frequency,
+            gradient_accumulation_step=self.gradient_accumulation_step,
+            layers_to_freeze=self.layers_to_freeze,
+            learning_rate=self.learning_rate,
+            learning_rate_scheduler=self.learning_rate_scheduler,
+            model_name=self.model_name,
+            momentum=self.momentum,
+            nesterov=self.nesterov,
+            number_of_epochs=self.number_of_epochs,
+            number_of_workers=self.number_of_workers,
+            optimizer=self.optimizer,
+            random_seed=self.random_seed,
+            step_lr_gamma=self.step_lr_gamma,
+            step_lr_step_size=self.step_lr_step_size,
+            training_batch_size=self.training_batch_size,
+            validation_batch_size=self.validation_batch_size,
+            warmup_cosine_lr_cycles=self.warmup_cosine_lr_cycles,
+            warmup_cosine_lr_warmup_epochs=self.warmup_cosine_lr_warmup_epochs,
+            weight_decay=self.weight_decay,
+            box_detections_per_image=self.box_detections_per_image,
+            box_score_threshold=self.box_score_threshold,
+            image_size=self.image_size,
+            max_size=self.max_size,
+            min_size=self.min_size,
+            model_size=self.model_size,
+            multi_scale=self.multi_scale,
+            nms_iou_threshold=self.nms_iou_threshold,
+            tile_grid_size=self.tile_grid_size,
+            tile_overlap_ratio=self.tile_overlap_ratio,
+            tile_predictions_nms_threshold=self.tile_predictions_nms_threshold,
+            validation_iou_threshold=self.validation_iou_threshold,
+            validation_metric_type=self.validation_metric_type,
+            log_training_metrics=self.log_training_metrics,
+            log_validation_loss=self.log_validation_loss,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestImageModelSettingsObjectDetection) -> "ImageModelSettingsObjectDetection":
+        return cls(
+            advanced_settings=obj.advanced_settings,
+            ams_gradient=obj.ams_gradient,
+            beta1=obj.beta1,
+            beta2=obj.beta2,
+            checkpoint_frequency=obj.checkpoint_frequency,
+            checkpoint_run_id=obj.checkpoint_run_id,
+            distributed=obj.distributed,
+            early_stopping=obj.early_stopping,
+            early_stopping_delay=obj.early_stopping_delay,
+            early_stopping_patience=obj.early_stopping_patience,
+            enable_onnx_normalization=obj.enable_onnx_normalization,
+            evaluation_frequency=obj.evaluation_frequency,
+            gradient_accumulation_step=obj.gradient_accumulation_step,
+            layers_to_freeze=obj.layers_to_freeze,
+            learning_rate=obj.learning_rate,
+            learning_rate_scheduler=obj.learning_rate_scheduler,
+            model_name=obj.model_name,
+            momentum=obj.momentum,
+            nesterov=obj.nesterov,
+            number_of_epochs=obj.number_of_epochs,
+            number_of_workers=obj.number_of_workers,
+            optimizer=obj.optimizer,
+            random_seed=obj.random_seed,
+            step_lr_gamma=obj.step_lr_gamma,
+            step_lr_step_size=obj.step_lr_step_size,
+            training_batch_size=obj.training_batch_size,
+            validation_batch_size=obj.validation_batch_size,
+            warmup_cosine_lr_cycles=obj.warmup_cosine_lr_cycles,
+            warmup_cosine_lr_warmup_epochs=obj.warmup_cosine_lr_warmup_epochs,
+            weight_decay=obj.weight_decay,
+            box_detections_per_image=obj.box_detections_per_image,
+            box_score_threshold=obj.box_score_threshold,
+            image_size=obj.image_size,
+            max_size=obj.max_size,
+            min_size=obj.min_size,
+            model_size=obj.model_size,
+            multi_scale=obj.multi_scale,
+            nms_iou_threshold=obj.nms_iou_threshold,
+            tile_grid_size=obj.tile_grid_size,
+            tile_overlap_ratio=obj.tile_overlap_ratio,
+            tile_predictions_nms_threshold=obj.tile_predictions_nms_threshold,
+            validation_iou_threshold=obj.validation_iou_threshold,
+            validation_metric_type=obj.validation_metric_type,
+            log_training_metrics=obj.log_training_metrics,
+            log_validation_loss=obj.log_validation_loss,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ImageModelSettingsObjectDetection):
+            return NotImplemented
+
+        return (
+            super().__eq__(other)
+            and self.box_detections_per_image == other.box_detections_per_image
+            and self.box_score_threshold == other.box_score_threshold
+            and self.image_size == other.image_size
+            and self.max_size == other.max_size
+            and self.min_size == other.min_size
+            and self.model_size == other.model_size
+            and self.multi_scale == other.multi_scale
+            and self.nms_iou_threshold == other.nms_iou_threshold
+            and self.tile_grid_size == other.tile_grid_size
+            and self.tile_overlap_ratio == other.tile_overlap_ratio
+            and self.tile_predictions_nms_threshold == other.tile_predictions_nms_threshold
+            and self.validation_iou_threshold == other.validation_iou_threshold
+            and self.validation_metric_type == other.validation_metric_type
+            and self.log_training_metrics == other.log_training_metrics
+            and self.log_validation_loss == other.log_validation_loss
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_object_detection_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_object_detection_job.py
new file mode 100644
index 00000000..f8d070d2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_object_detection_job.py
@@ -0,0 +1,240 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ImageObjectDetection as RestImageObjectDetection
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase, ObjectDetectionPrimaryMetrics, TaskType
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.image.automl_image_object_detection_base import AutoMLImageObjectDetectionBase
+from azure.ai.ml.entities._job.automl.image.image_limit_settings import ImageLimitSettings
+from azure.ai.ml.entities._job.automl.image.image_model_settings import ImageModelSettingsObjectDetection
+from azure.ai.ml.entities._job.automl.image.image_sweep_settings import ImageSweepSettings
+from azure.ai.ml.entities._util import load_from_dict
+
+
+class ImageObjectDetectionJob(AutoMLImageObjectDetectionBase):
+    """Configuration for AutoML Image Object Detection job.
+
+    :keyword primary_metric: The primary metric to use for optimization.
+    :paramtype primary_metric: Optional[str, ~azure.ai.ml.ObjectDetectionPrimaryMetrics]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+            :start-after: [START automl.automl_image_job.image_object_detection_job]
+            :end-before: [END automl.automl_image_job.image_object_detection_job]
+            :language: python
+            :dedent: 8
+            :caption: creating an automl image object detection job
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ObjectDetectionPrimaryMetrics.MEAN_AVERAGE_PRECISION
+
+    def __init__(
+        self,
+        *,
+        primary_metric: Optional[Union[str, ObjectDetectionPrimaryMetrics]] = None,
+        **kwargs: Any,
+    ) -> None:
+
+        # Extract any super class init settings
+        limits = kwargs.pop("limits", None)
+        sweep = kwargs.pop("sweep", None)
+        training_parameters = kwargs.pop("training_parameters", None)
+        search_space = kwargs.pop("search_space", None)
+
+        super().__init__(
+            task_type=TaskType.IMAGE_OBJECT_DETECTION,
+            limits=limits,
+            sweep=sweep,
+            training_parameters=training_parameters,
+            search_space=search_space,
+            **kwargs,
+        )
+
+        self.primary_metric = primary_metric or ImageObjectDetectionJob._DEFAULT_PRIMARY_METRIC
+
+    @property
+    def primary_metric(self) -> Union[str, ObjectDetectionPrimaryMetrics]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ObjectDetectionPrimaryMetrics]) -> None:
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+        self._primary_metric = (
+            ImageObjectDetectionJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ObjectDetectionPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        image_object_detection_task = RestImageObjectDetection(
+            target_column_name=self.target_column_name,
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            validation_data_size=self.validation_data_size,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            sweep_settings=self._sweep._to_rest_object() if self._sweep else None,
+            model_settings=self._training_parameters._to_rest_object() if self._training_parameters else None,
+            search_space=(
+                [entry._to_rest_object() for entry in self._search_space if entry is not None]
+                if self._search_space is not None
+                else None
+            ),
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        # resolve data inputs in rest object
+        self._resolve_data_inputs(image_object_detection_task)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=image_object_detection_task,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "ImageObjectDetectionJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestImageObjectDetection = properties.task_details
+
+        job_args_dict = {
+            "id": obj.id,
+            "name": obj.name,
+            "description": properties.description,
+            "tags": properties.tags,
+            "properties": properties.properties,
+            "experiment_name": properties.experiment_name,
+            "services": properties.services,
+            "status": properties.status,
+            "creation_context": obj.system_data,
+            "display_name": properties.display_name,
+            "compute": properties.compute_id,
+            "outputs": from_rest_data_outputs(properties.outputs),
+            "resources": properties.resources,
+            "identity": (
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            "queue_settings": properties.queue_settings,
+        }
+
+        image_object_detection_job = cls(
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            validation_data_size=task_details.validation_data_size,
+            limits=(
+                ImageLimitSettings._from_rest_object(task_details.limit_settings)
+                if task_details.limit_settings
+                else None
+            ),
+            sweep=(
+                ImageSweepSettings._from_rest_object(task_details.sweep_settings)
+                if task_details.sweep_settings
+                else None
+            ),
+            training_parameters=(
+                ImageModelSettingsObjectDetection._from_rest_object(task_details.model_settings)
+                if task_details.model_settings
+                else None
+            ),
+            search_space=cls._get_search_space_from_str(task_details.search_space),
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            **job_args_dict,
+        )
+
+        image_object_detection_job._restore_data_inputs()
+
+        return image_object_detection_job
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "ImageObjectDetectionJob":
+        from azure.ai.ml._schema.automl.image_vertical.image_object_detection import ImageObjectDetectionSchema
+        from azure.ai.ml._schema.pipeline.automl_node import ImageObjectDetectionNodeSchema
+
+        if kwargs.pop("inside_pipeline", False):
+            if context.get("inside_pipeline", None) is None:
+                context["inside_pipeline"] = True
+            loaded_data = load_from_dict(
+                ImageObjectDetectionNodeSchema,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        else:
+            loaded_data = load_from_dict(ImageObjectDetectionSchema, data, context, additional_message, **kwargs)
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "ImageObjectDetectionJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        data_settings = {
+            "training_data": loaded_data.pop("training_data"),
+            "target_column_name": loaded_data.pop("target_column_name"),
+            "validation_data": loaded_data.pop("validation_data", None),
+            "validation_data_size": loaded_data.pop("validation_data_size", None),
+        }
+        job = ImageObjectDetectionJob(**loaded_data)
+        job.set_data(**data_settings)
+        return job
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.image_vertical.image_object_detection import ImageObjectDetectionSchema
+        from azure.ai.ml._schema.pipeline.automl_node import ImageObjectDetectionNodeSchema
+
+        schema_dict: dict = {}
+        if inside_pipeline:
+            schema_dict = ImageObjectDetectionNodeSchema(
+                context={BASE_PATH_CONTEXT_KEY: "./", "inside_pipeline": True}
+            ).dump(self)
+        else:
+            schema_dict = ImageObjectDetectionSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+
+        return schema_dict
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ImageObjectDetectionJob):
+            return NotImplemented
+
+        if not super().__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_object_detection_search_space.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_object_detection_search_space.py
new file mode 100644
index 00000000..a9004d1e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_object_detection_search_space.py
@@ -0,0 +1,899 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=R0902,too-many-locals
+
+from typing import Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ImageModelDistributionSettingsObjectDetection
+from azure.ai.ml.entities._job.automl.search_space import SearchSpace
+from azure.ai.ml.entities._job.automl.search_space_utils import _convert_from_rest_object, _convert_to_rest_object
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+from azure.ai.ml.sweep import (
+    Choice,
+    LogNormal,
+    LogUniform,
+    Normal,
+    QLogNormal,
+    QLogUniform,
+    QNormal,
+    QUniform,
+    Randint,
+    Uniform,
+)
+
+
+class ImageObjectDetectionSearchSpace(RestTranslatableMixin):
+    """Search space for AutoML Image Object Detection and Image Instance Segmentation tasks.
+
+    :param ams_gradient: Enable AMSGrad when optimizer is 'adam' or 'adamw'.
+    :type ams_gradient: bool or ~azure.ai.ml.entities.SweepDistribution
+    :param beta1: Value of 'beta1' when optimizer is 'adam' or 'adamw'. Must be a float in the
+        range [0, 1].
+    :type beta1: float or ~azure.ai.ml.entities.SweepDistribution
+    :param beta2: Value of 'beta2' when optimizer is 'adam' or 'adamw'. Must be a float in the
+        range [0, 1].
+    :type beta2: float or ~azure.ai.ml.entities.SweepDistribution
+    :param distributed: Whether to use distributer training.
+    :type distributed: bool or ~azure.ai.ml.entities.SweepDistribution
+    :param early_stopping: Enable early stopping logic during training.
+    :type early_stopping: bool or ~azure.ai.ml.entities.SweepDistribution
+    :param early_stopping_delay: Minimum number of epochs or validation evaluations to wait
+        before primary metric improvement
+        is tracked for early stopping. Must be a positive integer.
+    :type early_stopping_delay: int or ~azure.ai.ml.entities.SweepDistribution
+    :param early_stopping_patience: Minimum number of epochs or validation evaluations with no
+        primary metric improvement before the run is stopped. Must be a positive integer.
+    :type early_stopping_patience: int or ~azure.ai.ml.entities.SweepDistribution
+    :param enable_onnx_normalization: Enable normalization when exporting ONNX model.
+    :type enable_onnx_normalization: bool or ~azure.ai.ml.entities.SweepDistribution
+    :param evaluation_frequency: Frequency to evaluate validation dataset to get metric scores.
+        Must be a positive integer.
+    :type evaluation_frequency: int or ~azure.ai.ml.entities.SweepDistribution
+    :param gradient_accumulation_step: Gradient accumulation means running a configured number of
+        "GradAccumulationStep" steps without updating the model weights while accumulating the gradients of those steps,
+        and then using the accumulated gradients to compute the weight updates. Must be a positive integer.
+    :type gradient_accumulation_step: int or ~azure.ai.ml.entities.SweepDistribution
+    :param layers_to_freeze: Number of layers to freeze for the model. Must be a positive
+        integer. For instance, passing 2 as value for 'seresnext' means freezing layer0 and layer1.
+        For a full list of models supported and details on layer freeze, please
+        see: https://learn.microsoft.com/azure/machine-learning/reference-automl-images-hyperparameters#model-agnostic-hyperparameters.    # pylint: disable=line-too-long
+    :type layers_to_freeze: int or ~azure.ai.ml.entities.SweepDistribution
+    :param learning_rate: Initial learning rate. Must be a float in the range [0, 1].
+        :type learning_rate: float or ~azure.ai.ml.entities.SweepDistribution
+    :param learning_rate_scheduler: Type of learning rate scheduler. Must be 'warmup_cosine' or
+        'step'.
+    :type learning_rate_scheduler: str or ~azure.ai.ml.entities.SweepDistribution
+    :param model_name: Name of the model to use for training.
+        For more information on the available models please visit the official documentation:
+        https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-image-models.
+    :type model_name: str or ~azure.ai.ml.entities.SweepDistribution
+    :param momentum: Value of momentum when optimizer is 'sgd'. Must be a float in the range [0,
+        1].
+    :type momentum: float or ~azure.ai.ml.entities.SweepDistribution
+    :param nesterov: Enable nesterov when optimizer is 'sgd'.
+    :type nesterov: bool or ~azure.ai.ml.entities.SweepDistribution
+    :param number_of_epochs: Number of training epochs. Must be a positive integer.
+    :type number_of_epochs: int or ~azure.ai.ml.entities.SweepDistribution
+    :param number_of_workers: Number of data loader workers. Must be a non-negative integer.
+    :type number_of_workers: int or ~azure.ai.ml.entities.SweepDistribution
+    :param optimizer: Type of optimizer. Must be either 'sgd', 'adam', or 'adamw'.
+    :type optimizer: str or ~azure.ai.ml.entities.SweepDistribution
+    :param random_seed: Random seed to be used when using deterministic training.
+    :type random_seed: int or ~azure.ai.ml.entities.SweepDistribution
+    :param step_lr_gamma: Value of gamma when learning rate scheduler is 'step'. Must be a float
+        in the range [0, 1].
+    :type step_lr_gamma: float or ~azure.ai.ml.entities.SweepDistribution
+    :param step_lr_step_size: Value of step size when learning rate scheduler is 'step'. Must be
+        a positive integer.
+    :type step_lr_step_size: int or ~azure.ai.ml.entities.SweepDistribution
+    :param training_batch_size: Training batch size. Must be a positive integer.
+    :type training_batch_size: int or ~azure.ai.ml.entities.SweepDistribution
+    :param validation_batch_size: Validation batch size. Must be a positive integer.
+    :type validation_batch_size: int or ~azure.ai.ml.entities.SweepDistribution
+    :param warmup_cosine_lr_cycles: Value of cosine cycle when learning rate scheduler is
+        'warmup_cosine'. Must be a float in the range [0, 1].
+    :type warmup_cosine_lr_cycles: float or ~azure.ai.ml.entities.SweepDistribution
+    :param warmup_cosine_lr_warmup_epochs: Value of warmup epochs when learning rate scheduler is
+        'warmup_cosine'. Must be a positive integer.
+    :type warmup_cosine_lr_warmup_epochs: int or ~azure.ai.ml.entities.SweepDistribution
+    :param weight_decay: Value of weight decay when optimizer is 'sgd', 'adam', or 'adamw'. Must
+        be a float in the range[0, 1].
+    :type weight_decay: int or ~azure.ai.ml.entities.SweepDistribution
+    :param box_detections_per_image: Maximum number of detections per image, for all classes.
+        Must be a positive integer. Note: This settings is not supported for the 'yolov5' algorithm.
+    :type box_detections_per_image: int or ~azure.ai.ml.entities.SweepDistribution
+    :param box_score_threshold: During inference, only return proposals with a classification
+        score greater than BoxScoreThreshold. Must be a float in the range[0, 1].
+    :type box_score_threshold: float or ~azure.ai.ml.entities.SweepDistribution
+    :param image_size: Image size for train and validation. Must be a positive integer.
+        Note: The training run may get into CUDA OOM if the size is too big.
+        Note: This settings is only supported for the 'yolov5' algorithm.
+    :type image_size: int or ~azure.ai.ml.entities.SweepDistribution
+    :param max_size: Maximum size of the image to be rescaled before feeding it to the backbone.
+        Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+        Note: This settings is not supported for the 'yolov5' algorithm.
+        :type max_size: int or ~azure.ai.ml.entities.SweepDistribution
+    :param min_size: Minimum size of the image to be rescaled before feeding it to the backbone.
+        Must be a positive integer. Note: training run may get into CUDA OOM if the size is too big.
+        Note: This settings is not supported for the 'yolov5' algorithm.
+    :type min_size: int or ~azure.ai.ml.entities.SweepDistribution
+    :param model_size: Model size. Must be 'small', 'medium', 'large', or 'extra_large'.
+        Note: training run may get into CUDA OOM if the model size is too big.
+        Note: This settings is only supported for the 'yolov5' algorithm.
+    :type model_size: str or ~azure.ai.ml.entities.SweepDistribution
+    :param multi_scale: Enable multi-scale image by varying image size by +/- 50%.
+        Note: training run may get into CUDA OOM if no sufficient GPU memory.
+        Note: This settings is only supported for the 'yolov5' algorithm.
+    :type multi_scale: bool or ~azure.ai.ml.entities.SweepDistribution
+    :param nms_iou_threshold: IOU threshold used during inference in NMS post processing. Must be
+        float in the range [0, 1].
+    :type nms_iou_threshold: float or ~azure.ai.ml.entities.SweepDistribution
+    :param tile_grid_size: The grid size to use for tiling each image. Note: TileGridSize must
+        not be None to enable small object detection logic. A string containing two integers in mxn format.
+    :type tile_grid_size: str or ~azure.ai.ml.entities.SweepDistribution
+    :param tile_overlap_ratio: Overlap ratio between adjacent tiles in each dimension. Must be
+        float in the range [0, 1).
+    :type tile_overlap_ratio: float or ~azure.ai.ml.entities.SweepDistribution
+    :param tile_predictions_nms_threshold: The IOU threshold to use to perform NMS while merging
+        predictions from tiles and image. Used in validation/ inference. Must be float in the range [0, 1].
+        NMS: Non-maximum suppression.
+    :type tile_predictions_nms_threshold: float or ~azure.ai.ml.entities.SweepDistribution
+    :param validation_iou_threshold: IOU threshold to use when computing validation metric. Must
+        be float in the range [0, 1].
+    :type validation_iou_threshold: float or ~azure.ai.ml.entities.SweepDistribution
+    :param validation_metric_type: Metric computation method to use for validation metrics. Must
+        be 'none', 'coco', 'voc', or 'coco_voc'.
+    :type validation_metric_type: str or ~azure.ai.ml.entities.SweepDistribution
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+            :start-after: [START automl.automl_image_job.image_object_detection_search_space]
+            :end-before: [END automl.automl_image_job.image_object_detection_search_space]
+            :language: python
+            :dedent: 8
+            :caption: Defining an automl image object detection or instance segmentation search space
+    """
+
+    def __init__(
+        self,
+        *,
+        ams_gradient: Optional[
+            Union[
+                bool,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        beta1: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        beta2: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        distributed: Optional[
+            Union[
+                bool,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        early_stopping: Optional[
+            Union[
+                bool,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        early_stopping_delay: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        early_stopping_patience: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        enable_onnx_normalization: Optional[
+            Union[
+                bool,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        evaluation_frequency: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        gradient_accumulation_step: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        layers_to_freeze: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        learning_rate: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        learning_rate_scheduler: Optional[
+            Union[
+                str, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        model_name: Optional[
+            Union[
+                str, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        momentum: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        nesterov: Optional[
+            Union[
+                bool,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        number_of_epochs: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        number_of_workers: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        optimizer: Optional[
+            Union[
+                str, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        random_seed: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        step_lr_gamma: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        step_lr_step_size: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        training_batch_size: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        validation_batch_size: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        warmup_cosine_lr_cycles: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        warmup_cosine_lr_warmup_epochs: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        weight_decay: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        box_detections_per_image: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        box_score_threshold: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        image_size: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        max_size: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        min_size: Optional[
+            Union[
+                int, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        model_size: Optional[
+            Union[
+                str, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        multi_scale: Optional[
+            Union[
+                bool,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        nms_iou_threshold: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        tile_grid_size: Optional[
+            Union[
+                str, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+        tile_overlap_ratio: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        tile_predictions_nms_threshold: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        validation_iou_threshold: Optional[
+            Union[
+                float,
+                Choice,
+                LogNormal,
+                LogUniform,
+                Normal,
+                QLogNormal,
+                QLogUniform,
+                QNormal,
+                QUniform,
+                Randint,
+                Uniform,
+            ]
+        ] = None,
+        validation_metric_type: Optional[
+            Union[
+                str, Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+            ]
+        ] = None,
+    ) -> None:
+        self.ams_gradient = ams_gradient
+        self.beta1 = beta1
+        self.beta2 = beta2
+        self.distributed = distributed
+        self.early_stopping = early_stopping
+        self.early_stopping_delay = early_stopping_delay
+        self.early_stopping_patience = early_stopping_patience
+        self.enable_onnx_normalization = enable_onnx_normalization
+        self.evaluation_frequency = evaluation_frequency
+        self.gradient_accumulation_step = gradient_accumulation_step
+        self.layers_to_freeze = layers_to_freeze
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.momentum = momentum
+        self.nesterov = nesterov
+        self.number_of_epochs = number_of_epochs
+        self.number_of_workers = number_of_workers
+        self.optimizer = optimizer
+        self.random_seed = random_seed
+        self.step_lr_gamma = step_lr_gamma
+        self.step_lr_step_size = step_lr_step_size
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_cosine_lr_cycles = warmup_cosine_lr_cycles
+        self.warmup_cosine_lr_warmup_epochs = warmup_cosine_lr_warmup_epochs
+        self.weight_decay = weight_decay
+        self.box_detections_per_image = box_detections_per_image
+        self.box_score_threshold = box_score_threshold
+        self.image_size = image_size
+        self.max_size = max_size
+        self.min_size = min_size
+        self.model_size = model_size
+        self.multi_scale = multi_scale
+        self.nms_iou_threshold = nms_iou_threshold
+        self.tile_grid_size = tile_grid_size
+        self.tile_overlap_ratio = tile_overlap_ratio
+        self.tile_predictions_nms_threshold = tile_predictions_nms_threshold
+        self.validation_iou_threshold = validation_iou_threshold
+        self.validation_metric_type = validation_metric_type
+
+    def _to_rest_object(self) -> ImageModelDistributionSettingsObjectDetection:
+        return ImageModelDistributionSettingsObjectDetection(
+            ams_gradient=_convert_to_rest_object(self.ams_gradient) if self.ams_gradient is not None else None,
+            beta1=_convert_to_rest_object(self.beta1) if self.beta1 is not None else None,
+            beta2=_convert_to_rest_object(self.beta2) if self.beta2 is not None else None,
+            distributed=_convert_to_rest_object(self.distributed) if self.distributed is not None else None,
+            early_stopping=_convert_to_rest_object(self.early_stopping) if self.early_stopping is not None else None,
+            early_stopping_delay=(
+                _convert_to_rest_object(self.early_stopping_delay) if self.early_stopping_delay is not None else None
+            ),
+            early_stopping_patience=(
+                _convert_to_rest_object(self.early_stopping_patience)
+                if self.early_stopping_patience is not None
+                else None
+            ),
+            enable_onnx_normalization=(
+                _convert_to_rest_object(self.enable_onnx_normalization)
+                if self.enable_onnx_normalization is not None
+                else None
+            ),
+            evaluation_frequency=(
+                _convert_to_rest_object(self.evaluation_frequency) if self.evaluation_frequency is not None else None
+            ),
+            gradient_accumulation_step=(
+                _convert_to_rest_object(self.gradient_accumulation_step)
+                if self.gradient_accumulation_step is not None
+                else None
+            ),
+            layers_to_freeze=(
+                _convert_to_rest_object(self.layers_to_freeze) if self.layers_to_freeze is not None else None
+            ),
+            learning_rate=_convert_to_rest_object(self.learning_rate) if self.learning_rate is not None else None,
+            learning_rate_scheduler=(
+                _convert_to_rest_object(self.learning_rate_scheduler)
+                if self.learning_rate_scheduler is not None
+                else None
+            ),
+            model_name=_convert_to_rest_object(self.model_name) if self.model_name is not None else None,
+            momentum=_convert_to_rest_object(self.momentum) if self.momentum is not None else None,
+            nesterov=_convert_to_rest_object(self.nesterov) if self.nesterov is not None else None,
+            number_of_epochs=(
+                _convert_to_rest_object(self.number_of_epochs) if self.number_of_epochs is not None else None
+            ),
+            number_of_workers=(
+                _convert_to_rest_object(self.number_of_workers) if self.number_of_workers is not None else None
+            ),
+            optimizer=_convert_to_rest_object(self.optimizer) if self.optimizer is not None else None,
+            random_seed=_convert_to_rest_object(self.random_seed) if self.random_seed is not None else None,
+            step_lr_gamma=_convert_to_rest_object(self.step_lr_gamma) if self.step_lr_gamma is not None else None,
+            step_lr_step_size=(
+                _convert_to_rest_object(self.step_lr_step_size) if self.step_lr_step_size is not None else None
+            ),
+            training_batch_size=(
+                _convert_to_rest_object(self.training_batch_size) if self.training_batch_size is not None else None
+            ),
+            validation_batch_size=(
+                _convert_to_rest_object(self.validation_batch_size) if self.validation_batch_size is not None else None
+            ),
+            warmup_cosine_lr_cycles=(
+                _convert_to_rest_object(self.warmup_cosine_lr_cycles)
+                if self.warmup_cosine_lr_cycles is not None
+                else None
+            ),
+            warmup_cosine_lr_warmup_epochs=(
+                _convert_to_rest_object(self.warmup_cosine_lr_warmup_epochs)
+                if self.warmup_cosine_lr_warmup_epochs is not None
+                else None
+            ),
+            weight_decay=_convert_to_rest_object(self.weight_decay) if self.weight_decay is not None else None,
+            box_detections_per_image=(
+                _convert_to_rest_object(self.box_detections_per_image)
+                if self.box_detections_per_image is not None
+                else None
+            ),
+            box_score_threshold=(
+                _convert_to_rest_object(self.box_score_threshold) if self.box_score_threshold is not None else None
+            ),
+            image_size=_convert_to_rest_object(self.image_size) if self.image_size is not None else None,
+            max_size=_convert_to_rest_object(self.max_size) if self.max_size is not None else None,
+            min_size=_convert_to_rest_object(self.min_size) if self.min_size is not None else None,
+            model_size=_convert_to_rest_object(self.model_size) if self.model_size is not None else None,
+            multi_scale=_convert_to_rest_object(self.multi_scale) if self.multi_scale is not None else None,
+            nms_iou_threshold=(
+                _convert_to_rest_object(self.nms_iou_threshold) if self.nms_iou_threshold is not None else None
+            ),
+            tile_grid_size=_convert_to_rest_object(self.tile_grid_size) if self.tile_grid_size is not None else None,
+            tile_overlap_ratio=(
+                _convert_to_rest_object(self.tile_overlap_ratio) if self.tile_overlap_ratio is not None else None
+            ),
+            tile_predictions_nms_threshold=(
+                _convert_to_rest_object(self.tile_predictions_nms_threshold)
+                if self.tile_predictions_nms_threshold is not None
+                else None
+            ),
+            validation_iou_threshold=(
+                _convert_to_rest_object(self.validation_iou_threshold)
+                if self.validation_iou_threshold is not None
+                else None
+            ),
+            validation_metric_type=(
+                _convert_to_rest_object(self.validation_metric_type)
+                if self.validation_metric_type is not None
+                else None
+            ),
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: ImageModelDistributionSettingsObjectDetection) -> "ImageObjectDetectionSearchSpace":
+        return cls(
+            ams_gradient=_convert_from_rest_object(obj.ams_gradient) if obj.ams_gradient is not None else None,
+            beta1=_convert_from_rest_object(obj.beta1) if obj.beta1 is not None else None,
+            beta2=_convert_from_rest_object(obj.beta2) if obj.beta2 is not None else None,
+            distributed=_convert_from_rest_object(obj.distributed) if obj.distributed is not None else None,
+            early_stopping=_convert_from_rest_object(obj.early_stopping) if obj.early_stopping is not None else None,
+            early_stopping_delay=(
+                _convert_from_rest_object(obj.early_stopping_delay) if obj.early_stopping_delay is not None else None
+            ),
+            early_stopping_patience=(
+                _convert_from_rest_object(obj.early_stopping_patience)
+                if obj.early_stopping_patience is not None
+                else None
+            ),
+            enable_onnx_normalization=(
+                _convert_from_rest_object(obj.enable_onnx_normalization)
+                if obj.enable_onnx_normalization is not None
+                else None
+            ),
+            evaluation_frequency=(
+                _convert_from_rest_object(obj.evaluation_frequency) if obj.evaluation_frequency is not None else None
+            ),
+            gradient_accumulation_step=(
+                _convert_from_rest_object(obj.gradient_accumulation_step)
+                if obj.gradient_accumulation_step is not None
+                else None
+            ),
+            layers_to_freeze=(
+                _convert_from_rest_object(obj.layers_to_freeze) if obj.layers_to_freeze is not None else None
+            ),
+            learning_rate=_convert_from_rest_object(obj.learning_rate) if obj.learning_rate is not None else None,
+            learning_rate_scheduler=(
+                _convert_from_rest_object(obj.learning_rate_scheduler)
+                if obj.learning_rate_scheduler is not None
+                else None
+            ),
+            model_name=_convert_from_rest_object(obj.model_name) if obj.model_name is not None else None,
+            momentum=_convert_from_rest_object(obj.momentum) if obj.momentum is not None else None,
+            nesterov=_convert_from_rest_object(obj.nesterov) if obj.nesterov is not None else None,
+            number_of_epochs=(
+                _convert_from_rest_object(obj.number_of_epochs) if obj.number_of_epochs is not None else None
+            ),
+            number_of_workers=(
+                _convert_from_rest_object(obj.number_of_workers) if obj.number_of_workers is not None else None
+            ),
+            optimizer=_convert_from_rest_object(obj.optimizer) if obj.optimizer is not None else None,
+            random_seed=_convert_from_rest_object(obj.random_seed) if obj.random_seed is not None else None,
+            step_lr_gamma=_convert_from_rest_object(obj.step_lr_gamma) if obj.step_lr_gamma is not None else None,
+            step_lr_step_size=(
+                _convert_from_rest_object(obj.step_lr_step_size) if obj.step_lr_step_size is not None else None
+            ),
+            training_batch_size=(
+                _convert_from_rest_object(obj.training_batch_size) if obj.training_batch_size is not None else None
+            ),
+            validation_batch_size=(
+                _convert_from_rest_object(obj.validation_batch_size) if obj.validation_batch_size is not None else None
+            ),
+            warmup_cosine_lr_cycles=(
+                _convert_from_rest_object(obj.warmup_cosine_lr_cycles)
+                if obj.warmup_cosine_lr_cycles is not None
+                else None
+            ),
+            warmup_cosine_lr_warmup_epochs=(
+                _convert_from_rest_object(obj.warmup_cosine_lr_warmup_epochs)
+                if obj.warmup_cosine_lr_warmup_epochs is not None
+                else None
+            ),
+            weight_decay=_convert_from_rest_object(obj.weight_decay) if obj.weight_decay is not None else None,
+            box_detections_per_image=(
+                _convert_from_rest_object(obj.box_detections_per_image)
+                if obj.box_detections_per_image is not None
+                else None
+            ),
+            box_score_threshold=(
+                _convert_from_rest_object(obj.box_score_threshold) if obj.box_score_threshold is not None else None
+            ),
+            image_size=_convert_from_rest_object(obj.image_size) if obj.image_size is not None else None,
+            max_size=_convert_from_rest_object(obj.max_size) if obj.max_size is not None else None,
+            min_size=_convert_from_rest_object(obj.min_size) if obj.min_size is not None else None,
+            model_size=_convert_from_rest_object(obj.model_size) if obj.model_size is not None else None,
+            multi_scale=_convert_from_rest_object(obj.multi_scale) if obj.multi_scale is not None else None,
+            nms_iou_threshold=(
+                _convert_from_rest_object(obj.nms_iou_threshold) if obj.nms_iou_threshold is not None else None
+            ),
+            tile_grid_size=_convert_from_rest_object(obj.tile_grid_size) if obj.tile_grid_size is not None else None,
+            tile_overlap_ratio=(
+                _convert_from_rest_object(obj.tile_overlap_ratio) if obj.tile_overlap_ratio is not None else None
+            ),
+            tile_predictions_nms_threshold=(
+                _convert_from_rest_object(obj.tile_predictions_nms_threshold)
+                if obj.tile_predictions_nms_threshold is not None
+                else None
+            ),
+            validation_iou_threshold=(
+                _convert_from_rest_object(obj.validation_iou_threshold)
+                if obj.validation_iou_threshold is not None
+                else None
+            ),
+            validation_metric_type=(
+                _convert_from_rest_object(obj.validation_metric_type)
+                if obj.validation_metric_type is not None
+                else None
+            ),
+        )
+
+    @classmethod
+    def _from_search_space_object(cls, obj: SearchSpace) -> "ImageObjectDetectionSearchSpace":
+        return cls(
+            ams_gradient=obj.ams_gradient if hasattr(obj, "ams_gradient") else None,
+            beta1=obj.beta1 if hasattr(obj, "beta1") else None,
+            beta2=obj.beta2 if hasattr(obj, "beta2") else None,
+            distributed=obj.distributed if hasattr(obj, "distributed") else None,
+            early_stopping=obj.early_stopping if hasattr(obj, "early_stopping") else None,
+            early_stopping_delay=obj.early_stopping_delay if hasattr(obj, "early_stopping_delay") else None,
+            early_stopping_patience=obj.early_stopping_patience if hasattr(obj, "early_stopping_patience") else None,
+            enable_onnx_normalization=(
+                obj.enable_onnx_normalization if hasattr(obj, "enable_onnx_normalization") else None
+            ),
+            evaluation_frequency=obj.evaluation_frequency if hasattr(obj, "evaluation_frequency") else None,
+            gradient_accumulation_step=(
+                obj.gradient_accumulation_step if hasattr(obj, "gradient_accumulation_step") else None
+            ),
+            layers_to_freeze=obj.layers_to_freeze if hasattr(obj, "layers_to_freeze") else None,
+            learning_rate=obj.learning_rate if hasattr(obj, "learning_rate") else None,
+            learning_rate_scheduler=obj.learning_rate_scheduler if hasattr(obj, "learning_rate_scheduler") else None,
+            model_name=obj.model_name if hasattr(obj, "model_name") else None,
+            momentum=obj.momentum if hasattr(obj, "momentum") else None,
+            nesterov=obj.nesterov if hasattr(obj, "nesterov") else None,
+            number_of_epochs=obj.number_of_epochs if hasattr(obj, "number_of_epochs") else None,
+            number_of_workers=obj.number_of_workers if hasattr(obj, "number_of_workers") else None,
+            optimizer=obj.optimizer if hasattr(obj, "optimizer") else None,
+            random_seed=obj.random_seed if hasattr(obj, "random_seed") else None,
+            step_lr_gamma=obj.step_lr_gamma if hasattr(obj, "step_lr_gamma") else None,
+            step_lr_step_size=obj.step_lr_step_size if hasattr(obj, "step_lr_step_size") else None,
+            training_batch_size=obj.training_batch_size if hasattr(obj, "training_batch_size") else None,
+            validation_batch_size=obj.validation_batch_size if hasattr(obj, "validation_batch_size") else None,
+            warmup_cosine_lr_cycles=obj.warmup_cosine_lr_cycles if hasattr(obj, "warmup_cosine_lr_cycles") else None,
+            warmup_cosine_lr_warmup_epochs=(
+                obj.warmup_cosine_lr_warmup_epochs if hasattr(obj, "warmup_cosine_lr_warmup_epochs") else None
+            ),
+            weight_decay=obj.weight_decay if hasattr(obj, "weight_decay") else None,
+            box_detections_per_image=obj.box_detections_per_image if hasattr(obj, "box_detections_per_image") else None,
+            box_score_threshold=obj.box_score_threshold if hasattr(obj, "box_score_threshold") else None,
+            image_size=obj.image_size if hasattr(obj, "image_size") else None,
+            max_size=obj.max_size if hasattr(obj, "max_size") else None,
+            min_size=obj.min_size if hasattr(obj, "min_size") else None,
+            model_size=obj.model_size if hasattr(obj, "model_size") else None,
+            multi_scale=obj.multi_scale if hasattr(obj, "multi_scale") else None,
+            nms_iou_threshold=obj.nms_iou_threshold if hasattr(obj, "nms_iou_threshold") else None,
+            tile_grid_size=obj.tile_grid_size if hasattr(obj, "tile_grid_size") else None,
+            tile_overlap_ratio=obj.tile_overlap_ratio if hasattr(obj, "tile_overlap_ratio") else None,
+            tile_predictions_nms_threshold=(
+                obj.tile_predictions_nms_threshold if hasattr(obj, "tile_predictions_nms_threshold") else None
+            ),
+            validation_iou_threshold=obj.validation_iou_threshold if hasattr(obj, "validation_iou_threshold") else None,
+            validation_metric_type=obj.validation_metric_type if hasattr(obj, "validation_metric_type") else None,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ImageObjectDetectionSearchSpace):
+            return NotImplemented
+
+        return (
+            self.ams_gradient == other.ams_gradient
+            and self.beta1 == other.beta1
+            and self.beta2 == other.beta2
+            and self.distributed == other.distributed
+            and self.early_stopping == other.early_stopping
+            and self.early_stopping_delay == other.early_stopping_delay
+            and self.early_stopping_patience == other.early_stopping_patience
+            and self.enable_onnx_normalization == other.enable_onnx_normalization
+            and self.evaluation_frequency == other.evaluation_frequency
+            and self.gradient_accumulation_step == other.gradient_accumulation_step
+            and self.layers_to_freeze == other.layers_to_freeze
+            and self.learning_rate == other.learning_rate
+            and self.learning_rate_scheduler == other.learning_rate_scheduler
+            and self.model_name == other.model_name
+            and self.momentum == other.momentum
+            and self.nesterov == other.nesterov
+            and self.number_of_epochs == other.number_of_epochs
+            and self.number_of_workers == other.number_of_workers
+            and self.optimizer == other.optimizer
+            and self.random_seed == other.random_seed
+            and self.step_lr_gamma == other.step_lr_gamma
+            and self.step_lr_step_size == other.step_lr_step_size
+            and self.training_batch_size == other.training_batch_size
+            and self.validation_batch_size == other.validation_batch_size
+            and self.warmup_cosine_lr_cycles == other.warmup_cosine_lr_cycles
+            and self.warmup_cosine_lr_warmup_epochs == other.warmup_cosine_lr_warmup_epochs
+            and self.weight_decay == other.weight_decay
+            and self.box_detections_per_image == other.box_detections_per_image
+            and self.box_score_threshold == other.box_score_threshold
+            and self.image_size == other.image_size
+            and self.max_size == other.max_size
+            and self.min_size == other.min_size
+            and self.model_size == other.model_size
+            and self.multi_scale == other.multi_scale
+            and self.nms_iou_threshold == other.nms_iou_threshold
+            and self.tile_grid_size == other.tile_grid_size
+            and self.tile_overlap_ratio == other.tile_overlap_ratio
+            and self.tile_predictions_nms_threshold == other.tile_predictions_nms_threshold
+            and self.validation_iou_threshold == other.validation_iou_threshold
+            and self.validation_metric_type == other.validation_metric_type
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_sweep_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_sweep_settings.py
new file mode 100644
index 00000000..b5e9ffaf
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/image/image_sweep_settings.py
@@ -0,0 +1,86 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ImageSweepSettings as RestImageSweepSettings
+from azure.ai.ml._restclient.v2023_04_01_preview.models import SamplingAlgorithmType
+from azure.ai.ml.entities._job.sweep.early_termination_policy import (
+    BanditPolicy,
+    EarlyTerminationPolicy,
+    MedianStoppingPolicy,
+    TruncationSelectionPolicy,
+)
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class ImageSweepSettings(RestTranslatableMixin):
+    """Sweep settings for all AutoML Image Verticals.
+
+    :keyword sampling_algorithm: Required. Type of the hyperparameter sampling.
+        algorithms. Possible values include: "Grid", "Random", "Bayesian".
+    :paramtype sampling_algorithm: Union[
+        str,
+        ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType.GRID,
+        ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType.BAYESIAN,
+        ~azure.mgmt.machinelearningservices.models.SamplingAlgorithmType.RANDOM
+
+    ]
+    :keyword early_termination: Type of early termination policy.
+    :paramtype early_termination: Union[
+
+        ~azure.mgmt.machinelearningservices.models.BanditPolicy,
+        ~azure.mgmt.machinelearningservices.models.MedianStoppingPolicy,
+        ~azure.mgmt.machinelearningservices.models.TruncationSelectionPolicy
+
+    ]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_image.py
+            :start-after: [START automl.automl_image_job.image_sweep_settings]
+            :end-before: [END automl.automl_image_job.image_sweep_settings]
+            :language: python
+            :dedent: 8
+            :caption: Defining the sweep settings for an automl image job.
+    """
+
+    def __init__(
+        self,
+        *,
+        sampling_algorithm: Union[
+            str, SamplingAlgorithmType.GRID, SamplingAlgorithmType.BAYESIAN, SamplingAlgorithmType.RANDOM
+        ],
+        early_termination: Optional[
+            Union[EarlyTerminationPolicy, BanditPolicy, MedianStoppingPolicy, TruncationSelectionPolicy]
+        ] = None,
+    ):
+        self.sampling_algorithm = sampling_algorithm
+        self.early_termination = early_termination
+
+    def _to_rest_object(self) -> RestImageSweepSettings:
+        return RestImageSweepSettings(
+            sampling_algorithm=self.sampling_algorithm,
+            early_termination=self.early_termination._to_rest_object() if self.early_termination else None,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestImageSweepSettings) -> "ImageSweepSettings":
+        return cls(
+            sampling_algorithm=obj.sampling_algorithm,
+            early_termination=(
+                EarlyTerminationPolicy._from_rest_object(obj.early_termination) if obj.early_termination else None
+            ),
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ImageSweepSettings):
+            return NotImplemented
+
+        return self.sampling_algorithm == other.sampling_algorithm and self.early_termination == other.early_termination
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/__init__.py
new file mode 100644
index 00000000..9be7b483
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/__init__.py
@@ -0,0 +1,25 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from .automl_nlp_job import AutoMLNLPJob
+from .nlp_featurization_settings import NlpFeaturizationSettings
+from .nlp_fixed_parameters import NlpFixedParameters
+from .nlp_limit_settings import NlpLimitSettings
+from .nlp_search_space import NlpSearchSpace
+from .nlp_sweep_settings import NlpSweepSettings
+from .text_classification_job import TextClassificationJob
+from .text_classification_multilabel_job import TextClassificationMultilabelJob
+from .text_ner_job import TextNerJob
+
+__all__ = [
+    "AutoMLNLPJob",
+    "NlpFeaturizationSettings",
+    "NlpFixedParameters",
+    "NlpLimitSettings",
+    "NlpSearchSpace",
+    "NlpSweepSettings",
+    "TextClassificationJob",
+    "TextClassificationMultilabelJob",
+    "TextNerJob",
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/automl_nlp_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/automl_nlp_job.py
new file mode 100644
index 00000000..f0b3baa8
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/automl_nlp_job.py
@@ -0,0 +1,467 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from abc import ABC
+from typing import Any, Dict, List, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    LogVerbosity,
+    NlpLearningRateScheduler,
+    SamplingAlgorithmType,
+)
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job.automl.automl_vertical import AutoMLVertical
+from azure.ai.ml.entities._job.automl.nlp.nlp_featurization_settings import NlpFeaturizationSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_fixed_parameters import NlpFixedParameters
+from azure.ai.ml.entities._job.automl.nlp.nlp_limit_settings import NlpLimitSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_search_space import NlpSearchSpace
+from azure.ai.ml.entities._job.automl.nlp.nlp_sweep_settings import NlpSweepSettings
+from azure.ai.ml.entities._job.automl.search_space import SearchSpace
+from azure.ai.ml.entities._job.automl.utils import cast_to_specific_search_space
+from azure.ai.ml.entities._job.sweep.early_termination_policy import EarlyTerminationPolicy
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+# pylint: disable=too-many-instance-attributes,protected-access
+class AutoMLNLPJob(AutoMLVertical, ABC):
+    """Base class for AutoML NLP jobs.
+
+    You should not instantiate this class directly. Instead you should
+    create classes for specific NLP Jobs.
+
+    :param task_type: NLP task type, must be one of 'TextClassification',
+        'TextClassificationMultilabel', or 'TextNER'
+    :type task_type: str
+    :param primary_metric: Primary metric to display from NLP job
+    :type primary_metric: str
+    :param training_data: Training data
+    :type training_data: Input
+    :param validation_data: Validation data
+    :type validation_data: Input
+    :param target_column_name: Column name of the target column, defaults to None
+    :type target_column_name: Optional[str]
+    :param log_verbosity: The degree of verbosity used in logging, defaults to None,
+        must be one of 'NotSet', 'Debug', 'Info', 'Warning', 'Error', 'Critical', or None
+    :type log_verbosity: Optional[str]
+    :param featurization: Featurization settings used for NLP job, defaults to None
+    :type featurization: Optional[~azure.ai.ml.automl.NlpFeaturizationSettings]
+    :param limits: Limit settings for NLP jobs, defaults to None
+    :type limits: Optional[~azure.ai.ml.automl.NlpLimitSettings]
+    :param sweep: Sweep settings used for NLP job, defaults to None
+    :type sweep: Optional[~azure.ai.ml.automl.NlpSweepSettings]
+    :param training_parameters: Fixed parameters for the training of all candidates.
+        , defaults to None
+    :type training_parameters: Optional[~azure.ai.ml.automl.NlpFixedParameters]
+    :param search_space: Search space(s) to sweep over for NLP sweep jobs, defaults to None
+    :type search_space: Optional[List[~azure.ai.ml.automl.NlpSearchSpace]]
+    """
+
+    def __init__(
+        self,
+        *,
+        task_type: str,
+        primary_metric: str,
+        training_data: Optional[Input],
+        validation_data: Optional[Input],
+        target_column_name: Optional[str] = None,
+        log_verbosity: Optional[str] = None,
+        featurization: Optional[NlpFeaturizationSettings] = None,
+        limits: Optional[NlpLimitSettings] = None,
+        sweep: Optional[NlpSweepSettings] = None,
+        training_parameters: Optional[NlpFixedParameters] = None,
+        search_space: Optional[List[NlpSearchSpace]] = None,
+        **kwargs: Any,
+    ):
+        self._training_parameters: Optional[NlpFixedParameters] = None
+
+        super().__init__(
+            task_type, training_data=training_data, validation_data=validation_data, **kwargs  # type: ignore
+        )
+        self.log_verbosity = log_verbosity
+        self._primary_metric: str = ""
+        self.primary_metric = primary_metric
+
+        self.target_column_name = target_column_name
+
+        self._featurization = featurization
+        self._limits = limits or NlpLimitSettings()
+        self._sweep = sweep
+        self.training_parameters = training_parameters  # via setter method.
+        self._search_space = search_space
+
+    @property
+    def training_parameters(self) -> Optional[NlpFixedParameters]:
+        """Parameters that are used for all submitted jobs.
+
+        :return: fixed training parameters for NLP jobs
+        :rtype: ~azure.ai.ml.automl.NlpFixedParameters
+        """
+        return self._training_parameters
+
+    @training_parameters.setter
+    def training_parameters(self, value: Union[Dict, NlpFixedParameters]) -> None:
+        if value is None:
+            self._training_parameters = None
+        elif isinstance(value, NlpFixedParameters):
+            self._training_parameters = value
+            # Convert parameters from snake case to enum.
+            self.set_training_parameters(learning_rate_scheduler=value.learning_rate_scheduler)
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for nlp training parameters."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_training_parameters(**value)
+
+    @property
+    def search_space(self) -> Optional[List[NlpSearchSpace]]:
+        """Search space(s) to sweep over for NLP sweep jobs
+
+        :return: list of search spaces to sweep over for NLP jobs
+        :rtype: List[~azure.ai.ml.automl.NlpSearchSpace]
+        """
+        return self._search_space
+
+    @search_space.setter
+    def search_space(self, value: Union[List[dict], List[SearchSpace]]) -> None:
+        if not isinstance(value, list):
+            msg = "Expected a list for search space."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+        all_dict_type = all(isinstance(item, dict) for item in value)
+        all_search_space_type = all(isinstance(item, SearchSpace) for item in value)
+
+        if not (all_search_space_type or all_dict_type):
+            msg = "Expected all items in the list to be either dictionaries or SearchSpace objects."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+        self._search_space = [
+            cast_to_specific_search_space(item, NlpSearchSpace, self.task_type) for item in value  # type: ignore
+        ]
+
+    @property
+    def primary_metric(self) -> str:
+        """Primary metric to display from NLP job
+
+        :return: primary metric to display
+        :rtype: str
+        """
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: str) -> None:
+        self._primary_metric = value
+
+    @property
+    def log_verbosity(self) -> LogVerbosity:
+        """Log verbosity configuration
+
+        :return: the degree of verbosity used in logging
+        :rtype: ~azure.mgmt.machinelearningservices.models.LogVerbosity
+        """
+        return self._log_verbosity
+
+    @log_verbosity.setter
+    def log_verbosity(self, value: Union[str, LogVerbosity]) -> None:
+        self._log_verbosity = None if value is None else LogVerbosity[camel_to_snake(value).upper()]
+
+    @property
+    def limits(self) -> NlpLimitSettings:
+        """Limit settings for NLP jobs
+
+        :return: limit configuration for NLP job
+        :rtype: ~azure.ai.ml.automl.NlpLimitSettings
+        """
+        return self._limits
+
+    @limits.setter
+    def limits(self, value: Union[Dict, NlpLimitSettings]) -> None:
+        if isinstance(value, NlpLimitSettings):
+            self._limits = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for limit settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_limits(**value)
+
+    @property
+    def sweep(self) -> Optional[NlpSweepSettings]:
+        """Sweep settings used for NLP job
+
+        :return: sweep settings
+        :rtype: ~azure.ai.ml.automl.NlpSweepSettings
+        """
+        return self._sweep
+
+    @sweep.setter
+    def sweep(self, value: Union[Dict, NlpSweepSettings]) -> None:
+        if isinstance(value, NlpSweepSettings):
+            self._sweep = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for sweep settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_sweep(**value)
+
+    @property
+    def featurization(self) -> Optional[NlpFeaturizationSettings]:
+        """Featurization settings used for NLP job
+
+        :return: featurization settings
+        :rtype: ~azure.ai.ml.automl.NlpFeaturizationSettings
+        """
+        return self._featurization
+
+    @featurization.setter
+    def featurization(self, value: Union[Dict, NlpFeaturizationSettings]) -> None:
+        if isinstance(value, NlpFeaturizationSettings):
+            self._featurization = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for featurization settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_featurization(**value)
+
+    def set_data(self, *, training_data: Input, target_column_name: str, validation_data: Input) -> None:
+        """Define data configuration for NLP job
+
+        :keyword training_data: Training data
+        :type training_data: ~azure.ai.ml.Input
+        :keyword target_column_name: Column name of the target column.
+        :type target_column_name: str
+        :keyword validation_data: Validation data
+        :type validation_data: ~azure.ai.ml.Input
+        """
+        # Properties for NlpVerticalDataSettings
+        self.target_column_name = target_column_name
+        self.training_data = training_data
+        self.validation_data = validation_data
+
+    def set_limits(
+        self,
+        *,
+        max_trials: int = 1,
+        max_concurrent_trials: int = 1,
+        max_nodes: int = 1,
+        timeout_minutes: Optional[int] = None,
+        trial_timeout_minutes: Optional[int] = None,
+    ) -> None:
+        """Define limit configuration for AutoML NLP job
+
+        :keyword max_trials: Maximum number of AutoML iterations, defaults to 1
+        :type max_trials: int, optional
+        :keyword max_concurrent_trials: Maximum number of concurrent AutoML iterations, defaults to 1
+        :type max_concurrent_trials: int, optional
+        :keyword max_nodes: Maximum number of nodes used for sweep, defaults to 1
+        :type max_nodes: int, optional
+        :keyword timeout_minutes: Timeout for the AutoML job, defaults to None
+        :type timeout_minutes: Optional[int]
+        :keyword trial_timeout_minutes: Timeout for each AutoML trial, defaults to None
+        :type trial_timeout_minutes: Optional[int]
+        """
+        self._limits = NlpLimitSettings(
+            max_trials=max_trials,
+            max_concurrent_trials=max_concurrent_trials,
+            max_nodes=max_nodes,
+            timeout_minutes=timeout_minutes,
+            trial_timeout_minutes=trial_timeout_minutes,
+        )
+
+    def set_sweep(
+        self,
+        *,
+        sampling_algorithm: Union[str, SamplingAlgorithmType],
+        early_termination: Optional[EarlyTerminationPolicy] = None,
+    ) -> None:
+        """Define sweep configuration for AutoML NLP job
+
+        :keyword sampling_algorithm: Required. Specifies type of hyperparameter sampling algorithm.
+            Possible values include: "Grid", "Random", and "Bayesian".
+        :type sampling_algorithm: Union[str, ~azure.ai.ml.automl.SamplingAlgorithmType]
+        :keyword early_termination: Optional. early termination policy to end poorly performing training candidates,
+            defaults to None.
+        :type early_termination: Optional[~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy]
+        """
+        if self._sweep:
+            self._sweep.sampling_algorithm = sampling_algorithm
+        else:
+            self._sweep = NlpSweepSettings(sampling_algorithm=sampling_algorithm)
+
+        self._sweep.early_termination = early_termination or self._sweep.early_termination
+
+    def set_training_parameters(
+        self,
+        *,
+        gradient_accumulation_steps: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[Union[str, NlpLearningRateScheduler]] = None,
+        model_name: Optional[str] = None,
+        number_of_epochs: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_ratio: Optional[float] = None,
+        weight_decay: Optional[float] = None,
+    ) -> None:
+        """Fix certain training parameters throughout the training procedure for all candidates.
+
+        :keyword gradient_accumulation_steps: number of steps over which to accumulate gradients before a backward
+            pass. This must be a positive integer., defaults to None
+        :type gradient_accumulation_steps: Optional[int]
+        :keyword learning_rate: initial learning rate. Must be a float in (0, 1)., defaults to None
+        :type learning_rate: Optional[float]
+        :keyword learning_rate_scheduler: the type of learning rate scheduler. Must choose from 'linear', 'cosine',
+            'cosine_with_restarts', 'polynomial', 'constant', and 'constant_with_warmup'., defaults to None
+        :type learning_rate_scheduler: Optional[Union[str, ~azure.ai.ml.automl.NlpLearningRateScheduler]]
+        :keyword model_name: the model name to use during training. Must choose from 'bert-base-cased',
+            'bert-base-uncased', 'bert-base-multilingual-cased', 'bert-base-german-cased', 'bert-large-cased',
+            'bert-large-uncased', 'distilbert-base-cased', 'distilbert-base-uncased', 'roberta-base', 'roberta-large',
+            'distilroberta-base', 'xlm-roberta-base', 'xlm-roberta-large', xlnet-base-cased', and 'xlnet-large-cased'.,
+            defaults to None
+        :type model_name: Optional[str]
+        :keyword number_of_epochs: the number of epochs to train with. Must be a positive integer., defaults to None
+        :type number_of_epochs: Optional[int]
+        :keyword training_batch_size: the batch size during training. Must be a positive integer., defaults to None
+        :type training_batch_size: Optional[int]
+        :keyword validation_batch_size: the batch size during validation. Must be a positive integer., defaults to None
+        :type validation_batch_size: Optional[int]
+        :keyword warmup_ratio: ratio of total training steps used for a linear warmup from 0 to learning_rate.
+            Must be a float in [0, 1]., defaults to None
+        :type warmup_ratio: Optional[float]
+        :keyword weight_decay: value of weight decay when optimizer is sgd, adam, or adamw. This must be a float in
+            the range [0, 1]., defaults to None
+        :type weight_decay: Optional[float]
+        """
+        self._training_parameters = self._training_parameters or NlpFixedParameters()
+
+        self._training_parameters.gradient_accumulation_steps = (
+            gradient_accumulation_steps
+            if gradient_accumulation_steps is not None
+            else self._training_parameters.gradient_accumulation_steps
+        )
+
+        self._training_parameters.learning_rate = (
+            learning_rate if learning_rate is not None else self._training_parameters.learning_rate
+        )
+
+        self._training_parameters.learning_rate_scheduler = (
+            NlpLearningRateScheduler[camel_to_snake(learning_rate_scheduler).upper()]
+            if learning_rate_scheduler is not None
+            else self._training_parameters.learning_rate_scheduler
+        )
+
+        self._training_parameters.model_name = (
+            model_name if model_name is not None else self._training_parameters.model_name
+        )
+
+        self._training_parameters.number_of_epochs = (
+            number_of_epochs if number_of_epochs is not None else self._training_parameters.number_of_epochs
+        )
+
+        self._training_parameters.training_batch_size = (
+            training_batch_size if training_batch_size is not None else self._training_parameters.training_batch_size
+        )
+
+        self._training_parameters.validation_batch_size = (
+            validation_batch_size
+            if validation_batch_size is not None
+            else self._training_parameters.validation_batch_size
+        )
+
+        self._training_parameters.warmup_ratio = (
+            warmup_ratio if warmup_ratio is not None else self._training_parameters.warmup_ratio
+        )
+
+        self._training_parameters.weight_decay = (
+            weight_decay if weight_decay is not None else self._training_parameters.weight_decay
+        )
+
+    def set_featurization(self, *, dataset_language: Optional[str] = None) -> None:
+        """Define featurization configuration for AutoML NLP job.
+
+        :keyword dataset_language: Language of the dataset, defaults to None
+        :type dataset_language: Optional[str]
+        """
+        self._featurization = NlpFeaturizationSettings(
+            dataset_language=dataset_language,
+        )
+
+    def extend_search_space(self, value: Union[SearchSpace, List[SearchSpace]]) -> None:
+        """Add (a) search space(s) for an AutoML NLP job.
+
+        :param value: either a SearchSpace object or a list of SearchSpace objects with nlp-specific parameters.
+        :type value: Union[~azure.ai.ml.automl.SearchSpace, List[~azure.ai.ml.automl.SearchSpace]]
+        """
+        self._search_space = self._search_space or []
+        if isinstance(value, list):
+            self._search_space.extend(
+                [cast_to_specific_search_space(item, NlpSearchSpace, self.task_type) for item in value]  # type: ignore
+            )
+        else:
+            self._search_space.append(
+                cast_to_specific_search_space(value, NlpSearchSpace, self.task_type)  # type: ignore
+            )
+
+    @classmethod
+    def _get_search_space_from_str(cls, search_space_str: Optional[str]) -> Optional[List]:
+        if search_space_str is not None:
+            return [NlpSearchSpace._from_rest_object(entry) for entry in search_space_str if entry is not None]
+        return None
+
+    def _restore_data_inputs(self) -> None:
+        """Restore MLTableJobInputs to Inputs within data_settings.
+
+        self.training_data and self.validation_data should reflect what user passed in (Input) Once we get response back
+        from service (as MLTableJobInput), we should set responsible ones back to Input
+        """
+        super()._restore_data_inputs()
+        self.training_data = self.training_data if self.training_data else None  # type: ignore
+        self.validation_data = self.validation_data if self.validation_data else None  # type: ignore
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, AutoMLNLPJob):
+            return NotImplemented
+
+        return (
+            self.primary_metric == other.primary_metric
+            and self.log_verbosity == other.log_verbosity
+            and self.training_data == other.training_data
+            and self.validation_data == other.validation_data
+            and self._featurization == other._featurization
+            and self._limits == other._limits
+            and self._sweep == other._sweep
+            and self._training_parameters == other._training_parameters
+            and self._search_space == other._search_space
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_featurization_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_featurization_settings.py
new file mode 100644
index 00000000..5649dea2
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_featurization_settings.py
@@ -0,0 +1,47 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    NlpVerticalFeaturizationSettings as RestNlpVerticalFeaturizationSettings,
+)
+from azure.ai.ml.entities._job.automl.featurization_settings import FeaturizationSettings, FeaturizationSettingsType
+
+
+class NlpFeaturizationSettings(FeaturizationSettings):
+    """Featurization settings for all AutoML NLP Verticals.
+
+    :ivar type: Specifies the type of FeaturizationSettings. Set automatically to "NLP" for this class.
+    :vartype type: str
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.nlp_featurization_settings]
+                :end-before: [END automl.nlp_featurization_settings]
+                :language: python
+                :dedent: 8
+                :caption: creating an nlp featurization settings
+    """
+
+    type = FeaturizationSettingsType.NLP
+
+    def _to_rest_object(self) -> RestNlpVerticalFeaturizationSettings:
+        return RestNlpVerticalFeaturizationSettings(
+            dataset_language=self.dataset_language,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestNlpVerticalFeaturizationSettings) -> "NlpFeaturizationSettings":
+        return cls(
+            dataset_language=obj.dataset_language,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, NlpFeaturizationSettings):
+            return NotImplemented
+
+        return super().__eq__(other)
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_fixed_parameters.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_fixed_parameters.py
new file mode 100644
index 00000000..13c594b6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_fixed_parameters.py
@@ -0,0 +1,117 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from typing import Optional
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import NlpFixedParameters as RestNlpFixedParameters
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class NlpFixedParameters(RestTranslatableMixin):
+    """Configuration of fixed parameters for all candidates of an AutoML NLP Job
+
+    :param gradient_accumulation_steps: number of steps over which to accumulate gradients before a backward
+            pass. This must be a positive integer, defaults to None
+    :type gradient_accumulation_steps: Optional[int]
+    :param learning_rate: initial learning rate. Must be a float in (0, 1), defaults to None
+    :type learning_rate: Optional[float]
+    :param learning_rate_scheduler: the type of learning rate scheduler. Must choose from 'linear', 'cosine',
+            'cosine_with_restarts', 'polynomial', 'constant', and 'constant_with_warmup', defaults to None
+    :type learning_rate_scheduler: Optional[str]
+    :param model_name: the model name to use during training. Must choose from 'bert-base-cased',
+            'bert-base-uncased', 'bert-base-multilingual-cased', 'bert-base-german-cased', 'bert-large-cased',
+            'bert-large-uncased', 'distilbert-base-cased', 'distilbert-base-uncased', 'roberta-base', 'roberta-large',
+            'distilroberta-base', 'xlm-roberta-base', 'xlm-roberta-large', xlnet-base-cased', and 'xlnet-large-cased',
+            defaults to None
+    :type model_name: Optional[str]
+    :param number_of_epochs: the number of epochs to train with. Must be a positive integer, defaults to None
+    :type number_of_epochs: Optional[int]
+    :param training_batch_size: the batch size during training. Must be a positive integer, defaults to None
+    :type training_batch_size: Optional[int]
+    :param validation_batch_size: the batch size during validation. Must be a positive integer, defaults to None
+    :type validation_batch_size: Optional[int]
+    :param warmup_ratio: ratio of total training steps used for a linear warmup from 0 to learning_rate.
+            Must be a float in [0, 1], defaults to None
+    :type warmup_ratio: Optional[float]
+    :param weight_decay: value of weight decay when optimizer is sgd, adam, or adamw. This must be a float in
+            the range [0, 1] defaults to None
+    :type weight_decay: Optional[float]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.nlp_fixed_parameters]
+                :end-before: [END automl.nlp_fixed_parameters]
+                :language: python
+                :dedent: 8
+                :caption: creating an nlp fixed parameters
+    """
+
+    def __init__(
+        self,
+        *,
+        gradient_accumulation_steps: Optional[int] = None,
+        learning_rate: Optional[float] = None,
+        learning_rate_scheduler: Optional[str] = None,
+        model_name: Optional[str] = None,
+        number_of_epochs: Optional[int] = None,
+        training_batch_size: Optional[int] = None,
+        validation_batch_size: Optional[int] = None,
+        warmup_ratio: Optional[float] = None,
+        weight_decay: Optional[float] = None,
+    ):
+        self.gradient_accumulation_steps = gradient_accumulation_steps
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.number_of_epochs = number_of_epochs
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_ratio = warmup_ratio
+        self.weight_decay = weight_decay
+
+    def _to_rest_object(self) -> RestNlpFixedParameters:
+        return RestNlpFixedParameters(
+            gradient_accumulation_steps=self.gradient_accumulation_steps,
+            learning_rate=self.learning_rate,
+            learning_rate_scheduler=self.learning_rate_scheduler,
+            model_name=self.model_name,
+            number_of_epochs=self.number_of_epochs,
+            training_batch_size=self.training_batch_size,
+            validation_batch_size=self.validation_batch_size,
+            warmup_ratio=self.warmup_ratio,
+            weight_decay=self.weight_decay,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestNlpFixedParameters) -> "NlpFixedParameters":
+        return cls(
+            gradient_accumulation_steps=obj.gradient_accumulation_steps,
+            learning_rate=obj.learning_rate,
+            learning_rate_scheduler=obj.learning_rate_scheduler,
+            model_name=obj.model_name,
+            number_of_epochs=obj.number_of_epochs,
+            training_batch_size=obj.training_batch_size,
+            validation_batch_size=obj.validation_batch_size,
+            warmup_ratio=obj.warmup_ratio,
+            weight_decay=obj.weight_decay,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, NlpFixedParameters):
+            return NotImplemented
+
+        return (
+            self.gradient_accumulation_steps == other.gradient_accumulation_steps
+            and self.learning_rate == other.learning_rate
+            and self.learning_rate_scheduler == other.learning_rate_scheduler
+            and self.model_name == other.model_name
+            and self.number_of_epochs == other.number_of_epochs
+            and self.training_batch_size == other.training_batch_size
+            and self.validation_batch_size == other.validation_batch_size
+            and self.warmup_ratio == other.warmup_ratio
+            and self.weight_decay == other.weight_decay
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_limit_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_limit_settings.py
new file mode 100644
index 00000000..1e99f4f0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_limit_settings.py
@@ -0,0 +1,79 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import NlpVerticalLimitSettings as RestNlpLimitSettings
+from azure.ai.ml._utils.utils import from_iso_duration_format_mins, to_iso_duration_format_mins
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class NlpLimitSettings(RestTranslatableMixin):
+    """Limit settings for all AutoML NLP Verticals.
+
+    :param max_concurrent_trials: Maximum number of concurrent AutoML iterations.
+    :type max_concurrent_trials: int
+    :param max_trials: Maximum number of AutoML iterations.
+    :type max_trials: int
+    :param timeout_minutes: AutoML job timeout.
+    :type timeout_minutes: int
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.nlp_limit_settings]
+                :end-before: [END automl.nlp_limit_settings]
+                :language: python
+                :dedent: 8
+                :caption: creating an nlp limit settings
+    """
+
+    def __init__(
+        self,
+        *,
+        max_concurrent_trials: Optional[int] = None,
+        max_trials: int = 1,
+        max_nodes: int = 1,
+        timeout_minutes: Optional[int] = None,
+        trial_timeout_minutes: Optional[int] = None,
+    ):
+        self.max_concurrent_trials = max_concurrent_trials
+        self.max_trials = max_trials
+        self.max_nodes = max_nodes
+        self.timeout_minutes = timeout_minutes
+        self.trial_timeout_minutes = trial_timeout_minutes
+
+    def _to_rest_object(self) -> RestNlpLimitSettings:
+        return RestNlpLimitSettings(
+            max_concurrent_trials=self.max_concurrent_trials,
+            max_trials=self.max_trials,
+            max_nodes=self.max_nodes,
+            timeout=to_iso_duration_format_mins(self.timeout_minutes),
+            trial_timeout=to_iso_duration_format_mins(self.trial_timeout_minutes),
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestNlpLimitSettings) -> "NlpLimitSettings":
+        return cls(
+            max_concurrent_trials=obj.max_concurrent_trials,
+            max_trials=obj.max_trials,
+            max_nodes=obj.max_nodes,
+            timeout_minutes=from_iso_duration_format_mins(obj.timeout),
+            trial_timeout_minutes=from_iso_duration_format_mins(obj.trial_timeout),
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, NlpLimitSettings):
+            return NotImplemented
+
+        return (
+            self.max_concurrent_trials == other.max_concurrent_trials
+            and self.max_trials == other.max_trials
+            and self.max_nodes == other.max_nodes
+            and self.timeout_minutes == other.timeout_minutes
+            and self.trial_timeout_minutes == other.trial_timeout_minutes
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_search_space.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_search_space.py
new file mode 100644
index 00000000..e4ad435f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_search_space.py
@@ -0,0 +1,185 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import NlpLearningRateScheduler, NlpParameterSubspace
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.constants import NlpModels
+from azure.ai.ml.entities._job.automl.search_space import SearchSpace
+from azure.ai.ml.entities._job.automl.search_space_utils import _convert_from_rest_object, _convert_to_rest_object
+from azure.ai.ml.entities._job.sweep.search_space import Choice, SweepDistribution
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class NlpSearchSpace(RestTranslatableMixin):
+    """Search space for AutoML NLP tasks.
+
+    :param gradient_accumulation_steps: number of steps over which to accumulate gradients before a backward
+        pass. This must be a positive integer., defaults to None
+    :type gradient_accumulation_steps: Optional[Union[int, SweepDistribution]]
+    :param learning_rate: initial learning rate. Must be a float in (0, 1), defaults to None
+    :type learning_rate: Optional[Union[float, SweepDistribution]]
+    :param learning_rate_scheduler: the type of learning rate scheduler. Must choose from 'linear', 'cosine',
+        'cosine_with_restarts', 'polynomial', 'constant', and 'constant_with_warmup', defaults to None
+    :type learning_rate_scheduler: Optional[Union[str, SweepDistribution]]
+    :param model_name: the model name to use during training. Must choose from 'bert-base-cased',
+        'bert-base-uncased', 'bert-base-multilingual-cased', 'bert-base-german-cased', 'bert-large-cased',
+        'bert-large-uncased', 'distilbert-base-cased', 'distilbert-base-uncased', 'roberta-base', 'roberta-large',
+        'distilroberta-base', 'xlm-roberta-base', 'xlm-roberta-large', xlnet-base-cased', and 'xlnet-large-cased',
+        defaults to None
+    :type model_name: Optional[Union[str, SweepDistribution]]
+    :param number_of_epochs: the number of epochs to train with. Must be a positive integer, defaults to None
+    :type number_of_epochs: Optional[Union[int, SweepDistribution]]
+    :param training_batch_size: the batch size during training. Must be a positive integer, defaults to None
+    :type training_batch_size: Optional[Union[int, SweepDistribution]]
+    :param validation_batch_size: the batch size during validation. Must be a positive integer, defaults to None
+    :type validation_batch_size: Optional[Union[int, SweepDistribution]]
+    :param warmup_ratio: ratio of total training steps used for a linear warmup from 0 to learning_rate.
+            Must be a float in [0, 1], defaults to None
+    :type warmup_ratio: Optional[Union[float, SweepDistribution]]
+    :param weight_decay: value of weight decay when optimizer is sgd, adam, or adamw. This must be a float in
+            the range [0, 1], defaults to None
+    :type weight_decay: Optional[Union[float, SweepDistribution]]
+
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.nlp_search_space]
+                :end-before: [END automl.nlp_search_space]
+                :language: python
+                :dedent: 8
+                :caption: creating an nlp search space
+    """
+
+    def __init__(
+        self,
+        *,
+        gradient_accumulation_steps: Optional[Union[int, SweepDistribution]] = None,
+        learning_rate: Optional[Union[float, SweepDistribution]] = None,
+        learning_rate_scheduler: Optional[Union[str, SweepDistribution]] = None,
+        model_name: Optional[Union[str, SweepDistribution]] = None,
+        number_of_epochs: Optional[Union[int, SweepDistribution]] = None,
+        training_batch_size: Optional[Union[int, SweepDistribution]] = None,
+        validation_batch_size: Optional[Union[int, SweepDistribution]] = None,
+        warmup_ratio: Optional[Union[float, SweepDistribution]] = None,
+        weight_decay: Optional[Union[float, SweepDistribution]] = None
+    ):
+        # Since we want customers to be able to specify enums as well rather than just strings, we need to access
+        # the enum values here before we serialize them ('NlpModels.BERT_BASE_CASED' vs. 'bert-base-cased').
+        if isinstance(learning_rate_scheduler, NlpLearningRateScheduler):
+            learning_rate_scheduler = camel_to_snake(learning_rate_scheduler.value)
+        elif isinstance(learning_rate_scheduler, Choice):
+            if learning_rate_scheduler.values is not None:
+                learning_rate_scheduler.values = [
+                    camel_to_snake(item.value) if isinstance(item, NlpLearningRateScheduler) else item
+                    for item in learning_rate_scheduler.values
+                ]
+
+        if isinstance(model_name, NlpModels):
+            model_name = model_name.value
+        elif isinstance(model_name, Choice):
+            if model_name.values is not None:
+                model_name.values = [item.value if isinstance(item, NlpModels) else item for item in model_name.values]
+
+        self.gradient_accumulation_steps = gradient_accumulation_steps
+        self.learning_rate = learning_rate
+        self.learning_rate_scheduler = learning_rate_scheduler
+        self.model_name = model_name
+        self.number_of_epochs = number_of_epochs
+        self.training_batch_size = training_batch_size
+        self.validation_batch_size = validation_batch_size
+        self.warmup_ratio = warmup_ratio
+        self.weight_decay = weight_decay
+
+    def _to_rest_object(self) -> NlpParameterSubspace:
+        return NlpParameterSubspace(
+            gradient_accumulation_steps=(
+                _convert_to_rest_object(self.gradient_accumulation_steps)
+                if self.gradient_accumulation_steps is not None
+                else None
+            ),
+            learning_rate=_convert_to_rest_object(self.learning_rate) if self.learning_rate is not None else None,
+            learning_rate_scheduler=(
+                _convert_to_rest_object(self.learning_rate_scheduler)
+                if self.learning_rate_scheduler is not None
+                else None
+            ),
+            model_name=_convert_to_rest_object(self.model_name) if self.model_name is not None else None,
+            number_of_epochs=(
+                _convert_to_rest_object(self.number_of_epochs) if self.number_of_epochs is not None else None
+            ),
+            training_batch_size=(
+                _convert_to_rest_object(self.training_batch_size) if self.training_batch_size is not None else None
+            ),
+            validation_batch_size=(
+                _convert_to_rest_object(self.validation_batch_size) if self.validation_batch_size is not None else None
+            ),
+            warmup_ratio=_convert_to_rest_object(self.warmup_ratio) if self.warmup_ratio is not None else None,
+            weight_decay=_convert_to_rest_object(self.weight_decay) if self.weight_decay is not None else None,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: NlpParameterSubspace) -> "NlpSearchSpace":
+        return cls(
+            gradient_accumulation_steps=(
+                _convert_from_rest_object(obj.gradient_accumulation_steps)
+                if obj.gradient_accumulation_steps is not None
+                else None
+            ),
+            learning_rate=_convert_from_rest_object(obj.learning_rate) if obj.learning_rate is not None else None,
+            learning_rate_scheduler=(
+                _convert_from_rest_object(obj.learning_rate_scheduler)
+                if obj.learning_rate_scheduler is not None
+                else None
+            ),
+            model_name=_convert_from_rest_object(obj.model_name) if obj.model_name is not None else None,
+            number_of_epochs=(
+                _convert_from_rest_object(obj.number_of_epochs) if obj.number_of_epochs is not None else None
+            ),
+            training_batch_size=(
+                _convert_from_rest_object(obj.training_batch_size) if obj.training_batch_size is not None else None
+            ),
+            validation_batch_size=(
+                _convert_from_rest_object(obj.validation_batch_size) if obj.validation_batch_size is not None else None
+            ),
+            warmup_ratio=_convert_from_rest_object(obj.warmup_ratio) if obj.warmup_ratio is not None else None,
+            weight_decay=_convert_from_rest_object(obj.weight_decay) if obj.weight_decay is not None else None,
+        )
+
+    @classmethod
+    def _from_search_space_object(cls, obj: SearchSpace) -> "NlpSearchSpace":
+        return cls(
+            gradient_accumulation_steps=(
+                obj.gradient_accumulation_steps if hasattr(obj, "gradient_accumulation_steps") else None
+            ),
+            learning_rate=obj.learning_rate if hasattr(obj, "learning_rate") else None,
+            learning_rate_scheduler=obj.learning_rate_scheduler if hasattr(obj, "learning_rate_scheduler") else None,
+            model_name=obj.model_name if hasattr(obj, "model_name") else None,
+            number_of_epochs=obj.number_of_epochs if hasattr(obj, "number_of_epochs") else None,
+            training_batch_size=obj.training_batch_size if hasattr(obj, "training_batch_size") else None,
+            validation_batch_size=obj.validation_batch_size if hasattr(obj, "validation_batch_size") else None,
+            warmup_ratio=obj.warmup_ratio if hasattr(obj, "warmup_ratio") else None,
+            weight_decay=obj.weight_decay if hasattr(obj, "weight_decay") else None,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, NlpSearchSpace):
+            return NotImplemented
+
+        return (
+            self.gradient_accumulation_steps == other.gradient_accumulation_steps
+            and self.learning_rate == other.learning_rate
+            and self.learning_rate_scheduler == other.learning_rate_scheduler
+            and self.model_name == other.model_name
+            and self.number_of_epochs == other.number_of_epochs
+            and self.training_batch_size == other.training_batch_size
+            and self.validation_batch_size == other.validation_batch_size
+            and self.warmup_ratio == other.warmup_ratio
+            and self.weight_decay == other.weight_decay
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_sweep_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_sweep_settings.py
new file mode 100644
index 00000000..e446a30c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/nlp_sweep_settings.py
@@ -0,0 +1,65 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import NlpSweepSettings as RestNlpSweepSettings
+from azure.ai.ml._restclient.v2023_04_01_preview.models import SamplingAlgorithmType
+from azure.ai.ml.entities._job.sweep.early_termination_policy import EarlyTerminationPolicy
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+# pylint: disable=protected-access
+class NlpSweepSettings(RestTranslatableMixin):
+    """Sweep settings for all AutoML NLP tasks.
+
+    :param sampling_algorithm: Required. Specifies type of hyperparameter sampling algorithm.
+        Possible values include: "Grid", "Random", and "Bayesian".
+    :type sampling_algorithm: Union[str, ~azure.ai.ml.automl.SamplingAlgorithmType]
+    :param early_termination: Early termination policy to end poorly performing training candidates,
+        defaults to None.
+    :type early_termination: Optional[~azure.mgmt.machinelearningservices.models.EarlyTerminationPolicy]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.nlp_sweep_settings]
+                :end-before: [END automl.nlp_sweep_settings]
+                :language: python
+                :dedent: 8
+                :caption: creating an nlp sweep settings
+    """
+
+    def __init__(
+        self,
+        *,
+        sampling_algorithm: Union[str, SamplingAlgorithmType],
+        early_termination: Optional[EarlyTerminationPolicy] = None,
+    ):
+        self.sampling_algorithm = sampling_algorithm
+        self.early_termination = early_termination
+
+    def _to_rest_object(self) -> RestNlpSweepSettings:
+        return RestNlpSweepSettings(
+            sampling_algorithm=self.sampling_algorithm,
+            early_termination=self.early_termination._to_rest_object() if self.early_termination else None,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestNlpSweepSettings) -> "NlpSweepSettings":
+        return cls(
+            sampling_algorithm=obj.sampling_algorithm,
+            early_termination=(
+                EarlyTerminationPolicy._from_rest_object(obj.early_termination) if obj.early_termination else None
+            ),
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, NlpSweepSettings):
+            return NotImplemented
+
+        return self.sampling_algorithm == other.sampling_algorithm and self.early_termination == other.early_termination
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_job.py
new file mode 100644
index 00000000..290f4f70
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_job.py
@@ -0,0 +1,248 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase, TaskType
+from azure.ai.ml._restclient.v2023_04_01_preview.models._azure_machine_learning_workspaces_enums import (
+    ClassificationPrimaryMetrics,
+)
+from azure.ai.ml._restclient.v2024_01_01_preview.models import TextClassification as RestTextClassification
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.nlp.automl_nlp_job import AutoMLNLPJob
+from azure.ai.ml.entities._job.automl.nlp.nlp_featurization_settings import NlpFeaturizationSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_fixed_parameters import NlpFixedParameters
+from azure.ai.ml.entities._job.automl.nlp.nlp_limit_settings import NlpLimitSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_sweep_settings import NlpSweepSettings
+from azure.ai.ml.entities._system_data import SystemData
+from azure.ai.ml.entities._util import load_from_dict
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._component.component import Component
+
+
+class TextClassificationJob(AutoMLNLPJob):
+    """Configuration for AutoML Text Classification Job.
+
+    :param target_column_name: The name of the target column, defaults to None
+    :type target_column_name: Optional[str]
+    :param training_data: Training data to be used for training, defaults to None
+    :type training_data: Optional[~azure.ai.ml.Input]
+    :param validation_data: Validation data to be used for evaluating the trained model, defaults to None
+    :type validation_data: Optional[~azure.ai.ml.Input]
+    :param primary_metric: The primary metric to be displayed, defaults to None
+    :type primary_metric: Optional[~azure.ai.ml.automl.ClassificationPrimaryMetrics]
+    :param log_verbosity: Log verbosity level, defaults to None
+    :type log_verbosity: Optional[str]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.automl_nlp_job.text_classification_job]
+                :end-before: [END automl.automl_nlp_job.text_classification_job]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl text classification job
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ClassificationPrimaryMetrics.ACCURACY
+
+    def __init__(
+        self,
+        *,
+        target_column_name: Optional[str] = None,
+        training_data: Optional[Input] = None,
+        validation_data: Optional[Input] = None,
+        primary_metric: Optional[ClassificationPrimaryMetrics] = None,
+        log_verbosity: Optional[str] = None,
+        **kwargs: Any
+    ):
+        super().__init__(
+            task_type=TaskType.TEXT_CLASSIFICATION,
+            primary_metric=primary_metric or TextClassificationJob._DEFAULT_PRIMARY_METRIC,
+            target_column_name=target_column_name,
+            training_data=training_data,
+            validation_data=validation_data,
+            log_verbosity=log_verbosity,
+            **kwargs,
+        )
+
+    @property
+    def primary_metric(self) -> Union[str, ClassificationPrimaryMetrics]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ClassificationPrimaryMetrics]) -> None:
+        """setter for primary metric
+
+        :param value: _description_
+        :type value: Union[str, ClassificationPrimaryMetrics]
+        """
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+
+        self._primary_metric = (
+            TextClassificationJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ClassificationPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        text_classification = RestTextClassification(
+            target_column_name=self.target_column_name,
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            sweep_settings=self._sweep._to_rest_object() if self._sweep else None,
+            fixed_parameters=self._training_parameters._to_rest_object() if self._training_parameters else None,
+            search_space=(
+                [entry._to_rest_object() for entry in self._search_space if entry is not None]
+                if self._search_space is not None
+                else None
+            ),
+            featurization_settings=self._featurization._to_rest_object() if self._featurization else None,
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        # resolve data inputs in rest object
+        self._resolve_data_inputs(text_classification)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=text_classification,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "TextClassificationJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestTextClassification = properties.task_details
+        assert isinstance(task_details, RestTextClassification)
+        limits = (
+            NlpLimitSettings._from_rest_object(task_details.limit_settings) if task_details.limit_settings else None
+        )
+        featurization = (
+            NlpFeaturizationSettings._from_rest_object(task_details.featurization_settings)
+            if task_details.featurization_settings
+            else None
+        )
+        sweep = NlpSweepSettings._from_rest_object(task_details.sweep_settings) if task_details.sweep_settings else None
+        training_parameters = (
+            NlpFixedParameters._from_rest_object(task_details.fixed_parameters)
+            if task_details.fixed_parameters
+            else None
+        )
+
+        text_classification_job = cls(
+            # ----- job specific params
+            id=obj.id,
+            name=obj.name,
+            description=properties.description,
+            tags=properties.tags,
+            properties=properties.properties,
+            experiment_name=properties.experiment_name,
+            services=properties.services,
+            status=properties.status,
+            creation_context=SystemData._from_rest_object(obj.system_data) if obj.system_data else None,
+            display_name=properties.display_name,
+            compute=properties.compute_id,
+            outputs=from_rest_data_outputs(properties.outputs),
+            resources=properties.resources,
+            # ----- task specific params
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            limits=limits,
+            sweep=sweep,
+            training_parameters=training_parameters,
+            search_space=cls._get_search_space_from_str(task_details.search_space),
+            featurization=featurization,
+            identity=(
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            queue_settings=properties.queue_settings,
+        )
+
+        text_classification_job._restore_data_inputs()
+
+        return text_classification_job
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "Component":
+        raise NotImplementedError()
+
+    @classmethod
+    def _load_from_dict(
+        cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any
+    ) -> "TextClassificationJob":
+        from azure.ai.ml._schema.automl.nlp_vertical.text_classification import TextClassificationSchema
+
+        if kwargs.pop("inside_pipeline", False):
+            from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextClassificationNode
+
+            loaded_data = load_from_dict(
+                AutoMLTextClassificationNode,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        else:
+            loaded_data = load_from_dict(TextClassificationSchema, data, context, additional_message, **kwargs)
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "TextClassificationJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        return TextClassificationJob(**loaded_data)
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.nlp_vertical.text_classification import TextClassificationSchema
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextClassificationNode
+
+        if inside_pipeline:
+            res_autoML: dict = AutoMLTextClassificationNode(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+            return res_autoML
+
+        res: dict = TextClassificationSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, TextClassificationJob):
+            return NotImplemented
+
+        if not super(TextClassificationJob, self).__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_multilabel_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_multilabel_job.py
new file mode 100644
index 00000000..ac19b451
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_classification_multilabel_job.py
@@ -0,0 +1,252 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ClassificationMultilabelPrimaryMetrics, JobBase, TaskType
+from azure.ai.ml._restclient.v2024_01_01_preview.models import (
+    TextClassificationMultilabel as RestTextClassificationMultilabel,
+)
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.nlp.automl_nlp_job import AutoMLNLPJob
+from azure.ai.ml.entities._job.automl.nlp.nlp_featurization_settings import NlpFeaturizationSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_fixed_parameters import NlpFixedParameters
+from azure.ai.ml.entities._job.automl.nlp.nlp_limit_settings import NlpLimitSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_sweep_settings import NlpSweepSettings
+from azure.ai.ml.entities._system_data import SystemData
+from azure.ai.ml.entities._util import load_from_dict
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._component.component import Component
+
+
+class TextClassificationMultilabelJob(AutoMLNLPJob):
+    """Configuration for AutoML Text Classification Multilabel Job.
+
+    :param target_column_name: The name of the target column, defaults to None
+    :type target_column_name: Optional[str]
+    :param training_data: Training data to be used for training, defaults to None
+    :type training_data: Optional[~azure.ai.ml.Input]
+    :param validation_data: Validation data to be used for evaluating the trained model, defaults to None
+    :type validation_data: Optional[~azure.ai.ml.Input]
+    :param primary_metric: The primary metric to be displayed., defaults to None
+    :type primary_metric: Optional[str]
+    :param log_verbosity: Log verbosity level, defaults to None
+    :type log_verbosity: Optional[str]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.text_classification_multilabel_job]
+                :end-before: [END automl.text_classification_multilabel_job]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl text classification multilabel job
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ClassificationMultilabelPrimaryMetrics.ACCURACY
+
+    def __init__(
+        self,
+        *,
+        target_column_name: Optional[str] = None,
+        training_data: Optional[Input] = None,
+        validation_data: Optional[Input] = None,
+        primary_metric: Optional[str] = None,
+        log_verbosity: Optional[str] = None,
+        **kwargs: Any
+    ):
+        super().__init__(
+            task_type=TaskType.TEXT_CLASSIFICATION_MULTILABEL,
+            primary_metric=primary_metric or TextClassificationMultilabelJob._DEFAULT_PRIMARY_METRIC,
+            target_column_name=target_column_name,
+            training_data=training_data,
+            validation_data=validation_data,
+            log_verbosity=log_verbosity,
+            **kwargs,
+        )
+
+    @property
+    def primary_metric(self) -> Union[str, ClassificationMultilabelPrimaryMetrics]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ClassificationMultilabelPrimaryMetrics]) -> None:
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+
+        self._primary_metric = (
+            TextClassificationMultilabelJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ClassificationMultilabelPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        text_classification_multilabel = RestTextClassificationMultilabel(
+            target_column_name=self.target_column_name,
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            sweep_settings=self._sweep._to_rest_object() if self._sweep else None,
+            fixed_parameters=self._training_parameters._to_rest_object() if self._training_parameters else None,
+            search_space=(
+                [entry._to_rest_object() for entry in self._search_space if entry is not None]
+                if self._search_space is not None
+                else None
+            ),
+            featurization_settings=self._featurization._to_rest_object() if self._featurization else None,
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        # resolve data inputs in rest object
+        self._resolve_data_inputs(text_classification_multilabel)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=text_classification_multilabel,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "TextClassificationMultilabelJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestTextClassificationMultilabel = properties.task_details
+        assert isinstance(task_details, RestTextClassificationMultilabel)
+        limits = (
+            NlpLimitSettings._from_rest_object(task_details.limit_settings) if task_details.limit_settings else None
+        )
+        featurization = (
+            NlpFeaturizationSettings._from_rest_object(task_details.featurization_settings)
+            if task_details.featurization_settings
+            else None
+        )
+        sweep = NlpSweepSettings._from_rest_object(task_details.sweep_settings) if task_details.sweep_settings else None
+        training_parameters = (
+            NlpFixedParameters._from_rest_object(task_details.fixed_parameters)
+            if task_details.fixed_parameters
+            else None
+        )
+
+        text_classification_multilabel_job = cls(
+            # ----- job specific params
+            id=obj.id,
+            name=obj.name,
+            description=properties.description,
+            tags=properties.tags,
+            properties=properties.properties,
+            experiment_name=properties.experiment_name,
+            services=properties.services,
+            status=properties.status,
+            creation_context=SystemData._from_rest_object(obj.system_data) if obj.system_data else None,
+            display_name=properties.display_name,
+            compute=properties.compute_id,
+            outputs=from_rest_data_outputs(properties.outputs),
+            resources=properties.resources,
+            # ----- task specific params
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            limits=limits,
+            sweep=sweep,
+            training_parameters=training_parameters,
+            search_space=cls._get_search_space_from_str(task_details.search_space),
+            featurization=featurization,
+            identity=(
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            queue_settings=properties.queue_settings,
+        )
+
+        text_classification_multilabel_job._restore_data_inputs()
+
+        return text_classification_multilabel_job
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "Component":
+        raise NotImplementedError()
+
+    @classmethod
+    def _load_from_dict(
+        cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any
+    ) -> "TextClassificationMultilabelJob":
+        from azure.ai.ml._schema.automl.nlp_vertical.text_classification_multilabel import (
+            TextClassificationMultilabelSchema,
+        )
+
+        if kwargs.pop("inside_pipeline", False):
+            from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextClassificationMultilabelNode
+
+            loaded_data = load_from_dict(
+                AutoMLTextClassificationMultilabelNode,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        else:
+            loaded_data = load_from_dict(
+                TextClassificationMultilabelSchema,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "TextClassificationMultilabelJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        return TextClassificationMultilabelJob(**loaded_data)
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.nlp_vertical.text_classification_multilabel import (
+            TextClassificationMultilabelSchema,
+        )
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextClassificationMultilabelNode
+
+        if inside_pipeline:
+            res_autoML: dict = AutoMLTextClassificationMultilabelNode(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+            return res_autoML
+
+        res: dict = TextClassificationMultilabelSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, TextClassificationMultilabelJob):
+            return NotImplemented
+
+        if not super(TextClassificationMultilabelJob, self).__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_ner_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_ner_job.py
new file mode 100644
index 00000000..a87965f1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/nlp/text_ner_job.py
@@ -0,0 +1,231 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase, TaskType
+from azure.ai.ml._restclient.v2023_04_01_preview.models._azure_machine_learning_workspaces_enums import (
+    ClassificationPrimaryMetrics,
+)
+from azure.ai.ml._restclient.v2024_01_01_preview.models import TextNer as RestTextNER
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.nlp.automl_nlp_job import AutoMLNLPJob
+from azure.ai.ml.entities._job.automl.nlp.nlp_featurization_settings import NlpFeaturizationSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_fixed_parameters import NlpFixedParameters
+from azure.ai.ml.entities._job.automl.nlp.nlp_limit_settings import NlpLimitSettings
+from azure.ai.ml.entities._job.automl.nlp.nlp_sweep_settings import NlpSweepSettings
+from azure.ai.ml.entities._system_data import SystemData
+from azure.ai.ml.entities._util import load_from_dict
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._component.component import Component
+
+
+class TextNerJob(AutoMLNLPJob):
+    """Configuration for AutoML Text NER Job.
+
+    :param training_data: Training data to be used for training, defaults to None
+    :type training_data: Optional[~azure.ai.ml.Input]
+    :param validation_data: Validation data to be used for evaluating the trained model,
+        defaults to None
+    :type validation_data: Optional[~azure.ai.ml.Input]
+    :param primary_metric: The primary metric to be displayed, defaults to None
+    :type primary_metric: Optional[str]
+    :param log_verbosity: Log verbosity level, defaults to None
+    :type log_verbosity: Optional[str]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_automl_nlp.py
+                :start-after: [START automl.text_ner_job]
+                :end-before: [END automl.text_ner_job]
+                :language: python
+                :dedent: 8
+                :caption: creating an automl text ner job
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ClassificationPrimaryMetrics.ACCURACY
+
+    def __init__(
+        self,
+        *,
+        training_data: Optional[Input] = None,
+        validation_data: Optional[Input] = None,
+        primary_metric: Optional[str] = None,
+        log_verbosity: Optional[str] = None,
+        **kwargs: Any
+    ):
+        super(TextNerJob, self).__init__(
+            task_type=TaskType.TEXT_NER,
+            primary_metric=primary_metric or TextNerJob._DEFAULT_PRIMARY_METRIC,
+            training_data=training_data,
+            validation_data=validation_data,
+            log_verbosity=log_verbosity,
+            **kwargs,
+        )
+
+    @property
+    def primary_metric(self) -> Union[str, ClassificationPrimaryMetrics]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ClassificationPrimaryMetrics]) -> None:
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+
+        self._primary_metric = (
+            TextNerJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ClassificationPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        text_ner = RestTextNER(
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            sweep_settings=self._sweep._to_rest_object() if self._sweep else None,
+            fixed_parameters=self._training_parameters._to_rest_object() if self._training_parameters else None,
+            search_space=(
+                [entry._to_rest_object() for entry in self._search_space if entry is not None]
+                if self._search_space is not None
+                else None
+            ),
+            featurization_settings=self._featurization._to_rest_object() if self._featurization else None,
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        # resolve data inputs in rest object
+        self._resolve_data_inputs(text_ner)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=text_ner,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "TextNerJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestTextNER = properties.task_details
+        assert isinstance(task_details, RestTextNER)
+        limits = (
+            NlpLimitSettings._from_rest_object(task_details.limit_settings) if task_details.limit_settings else None
+        )
+        featurization = (
+            NlpFeaturizationSettings._from_rest_object(task_details.featurization_settings)
+            if task_details.featurization_settings
+            else None
+        )
+        sweep = NlpSweepSettings._from_rest_object(task_details.sweep_settings) if task_details.sweep_settings else None
+        training_parameters = (
+            NlpFixedParameters._from_rest_object(task_details.fixed_parameters)
+            if task_details.fixed_parameters
+            else None
+        )
+
+        text_ner_job = cls(
+            # ----- job specific params
+            id=obj.id,
+            name=obj.name,
+            description=properties.description,
+            tags=properties.tags,
+            properties=properties.properties,
+            experiment_name=properties.experiment_name,
+            services=properties.services,
+            status=properties.status,
+            creation_context=SystemData._from_rest_object(obj.system_data) if obj.system_data else None,
+            display_name=properties.display_name,
+            compute=properties.compute_id,
+            outputs=from_rest_data_outputs(properties.outputs),
+            resources=properties.resources,
+            # ----- task specific params
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            limits=limits,
+            sweep=sweep,
+            training_parameters=training_parameters,
+            search_space=cls._get_search_space_from_str(task_details.search_space),
+            featurization=featurization,
+            identity=(
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            queue_settings=properties.queue_settings,
+        )
+
+        text_ner_job._restore_data_inputs()
+
+        return text_ner_job
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "Component":
+        raise NotImplementedError()
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "TextNerJob":
+        from azure.ai.ml._schema.automl.nlp_vertical.text_ner import TextNerSchema
+
+        if kwargs.pop("inside_pipeline", False):
+            from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextNerNode
+
+            loaded_data = load_from_dict(AutoMLTextNerNode, data, context, additional_message, **kwargs)
+        else:
+            loaded_data = load_from_dict(TextNerSchema, data, context, additional_message, **kwargs)
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "TextNerJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        return TextNerJob(**loaded_data)
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.nlp_vertical.text_ner import TextNerSchema
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLTextNerNode
+
+        if inside_pipeline:
+            res_autoML: dict = AutoMLTextNerNode(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+            return res_autoML
+
+        res: dict = TextNerSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, TextNerJob):
+            return NotImplemented
+
+        if not super(TextNerJob, self).__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/search_space.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/search_space.py
new file mode 100644
index 00000000..a958de56
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/search_space.py
@@ -0,0 +1,14 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+
+from typing import Any
+
+
+class SearchSpace:
+    """SearchSpace class for AutoML verticals."""
+
+    def __init__(self, **kwargs: Any) -> None:
+        for k, v in kwargs.items():
+            self.__setattr__(k, v)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/search_space_utils.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/search_space_utils.py
new file mode 100644
index 00000000..732030d4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/search_space_utils.py
@@ -0,0 +1,276 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import re
+from typing import Any, List, Union
+
+from marshmallow import fields
+
+from azure.ai.ml._schema._sweep.search_space import (
+    ChoiceSchema,
+    NormalSchema,
+    QNormalSchema,
+    QUniformSchema,
+    RandintSchema,
+    UniformSchema,
+)
+from azure.ai.ml._schema.core.fields import DumpableIntegerField, DumpableStringField, NestedField, UnionField
+from azure.ai.ml._utils.utils import float_to_str
+from azure.ai.ml.constants._job.sweep import SearchSpace
+from azure.ai.ml.entities._job.sweep.search_space import (
+    Choice,
+    LogNormal,
+    LogUniform,
+    Normal,
+    QLogNormal,
+    QLogUniform,
+    QNormal,
+    QUniform,
+    Randint,
+    SweepDistribution,
+    Uniform,
+)
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+def _convert_to_rest_object(sweep_distribution: Union[bool, int, float, str, SweepDistribution]) -> str:
+    if isinstance(sweep_distribution, float):
+        # Float requires some special handling for small values that get auto-represented with scientific notation.
+        res: str = float_to_str(sweep_distribution)
+        return res
+    if not isinstance(sweep_distribution, SweepDistribution):
+        # Convert [bool, float, str] types to str
+        return str(sweep_distribution)
+
+    rest_object = sweep_distribution._to_rest_object()
+    if not isinstance(rest_object, list):
+        msg = "Rest Object for sweep distribution should be a list."
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.AUTOML,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+    if len(rest_object) <= 1:
+        msg = "Rest object for sweep distribution should contain at least two elements."
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.AUTOML,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+    sweep_distribution_type = rest_object[0]
+    sweep_distribution_args = []
+
+    if not isinstance(rest_object[1], list):
+        msg = "The second element of Rest object for sweep distribution should be a list."
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.AUTOML,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+    if sweep_distribution_type == SearchSpace.CHOICE:
+        # Rest objects for choice distribution are of format ["choice", [[0, 1, 2]]]
+        if not isinstance(rest_object[1][0], list):
+            msg = "The second element of Rest object for choice distribution should be a list of list."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        for value in rest_object[1][0]:
+            if isinstance(value, str):
+                sweep_distribution_args.append("'" + value + "'")
+            elif isinstance(value, float):
+                sweep_distribution_args.append(float_to_str(value))
+            else:
+                sweep_distribution_args.append(str(value))
+    else:
+        for value in rest_object[1]:
+            if isinstance(value, float):
+                sweep_distribution_args.append(float_to_str(value))
+            else:
+                sweep_distribution_args.append(str(value))
+
+    sweep_distribution_str: str = sweep_distribution_type + "("
+    sweep_distribution_str += ",".join(sweep_distribution_args)
+    sweep_distribution_str += ")"
+    return sweep_distribution_str
+
+
+def _is_int(value: str) -> bool:
+    try:
+        int(value)
+        return True
+    except ValueError:
+        return False
+
+
+def _is_float(value: str) -> bool:
+    try:
+        float(value)
+        return True
+    except ValueError:
+        return False
+
+
+def _get_type_inferred_value(value: str) -> Union[bool, int, float, str]:
+    value = value.strip()
+    if _is_int(value):
+        # Int
+        return int(value)
+    if _is_float(value):
+        # Float
+        return float(value)
+    if value in ["True", "False"]:
+        # Convert "True", "False" to python boolean literals
+        return value == "True"
+    # string value. Remove quotes before returning.
+    return value.strip("'\"")
+
+
+def _convert_from_rest_object(
+    sweep_distribution_str: str,
+) -> Any:
+    # sweep_distribution_str can be a distribution like "choice('vitb16r224', 'vits16r224')" or
+    # a single value like "True", "1", "1.0567", "vitb16r224"
+
+    sweep_distribution_str = sweep_distribution_str.strip()
+    # Filter by the delimiters and remove splits that are empty strings
+    sweep_distribution_separated = list(filter(None, re.split("[ ,()]+", sweep_distribution_str)))
+
+    if len(sweep_distribution_separated) == 1:
+        # Single value.
+        return _get_type_inferred_value(sweep_distribution_separated[0])
+
+    # Distribution string
+    sweep_distribution_type = sweep_distribution_separated[0].strip().lower()
+    sweep_distribution_args: List = []
+    for value in sweep_distribution_separated[1:]:
+        sweep_distribution_args.append(_get_type_inferred_value(value))
+
+    if sweep_distribution_type == SearchSpace.CHOICE:
+        sweep_distribution_args = [sweep_distribution_args]  # Choice values are list of lists
+
+    sweep_distribution = SweepDistribution._from_rest_object([sweep_distribution_type, sweep_distribution_args])
+    return sweep_distribution
+
+
+def _convert_sweep_dist_dict_to_str_dict(sweep_distribution: dict) -> dict:
+    for k, sweep_dist_dict in sweep_distribution.items():
+        if sweep_dist_dict is not None:
+            sweep_distribution[k] = _convert_sweep_dist_dict_item_to_str(sweep_dist_dict)
+    return sweep_distribution
+
+
+class ChoicePlusSchema(ChoiceSchema):
+    """Choice schema that allows boolean values also"""
+
+    values = fields.List(
+        UnionField(
+            [
+                DumpableIntegerField(strict=True),
+                DumpableStringField(),
+                fields.Float(),
+                fields.Dict(
+                    keys=fields.Str(),
+                    values=UnionField(
+                        [
+                            NestedField("ChoicePlusSchema"),
+                            NestedField(NormalSchema()),
+                            NestedField(QNormalSchema()),
+                            NestedField(RandintSchema()),
+                            NestedField(UniformSchema()),
+                            NestedField(QUniformSchema()),
+                            DumpableIntegerField(strict=True),
+                            fields.Float(),
+                            fields.Str(),
+                            fields.Boolean(),
+                        ]
+                    ),
+                ),
+                fields.Boolean(),
+            ]
+        )
+    )
+
+
+def _convert_sweep_dist_dict_item_to_str(sweep_distribution: Union[bool, int, float, str, dict]) -> str:
+    # Convert a Sweep Distribution dict to Sweep Distribution string
+    # Eg. {type: 'choice', values: ['vitb16r224','vits16r224']} => "Choice('vitb16r224','vits16r224')"
+    if isinstance(sweep_distribution, dict):
+        sweep_dist_type = sweep_distribution["type"]
+        if sweep_dist_type == SearchSpace.CHOICE:
+            sweep_dist_obj = ChoicePlusSchema().load(sweep_distribution)  # pylint: disable=no-member
+        elif sweep_dist_type in SearchSpace.UNIFORM_LOGUNIFORM:
+            sweep_dist_obj = UniformSchema().load(sweep_distribution)  # pylint: disable=no-member
+        elif sweep_dist_type in SearchSpace.NORMAL_LOGNORMAL:
+            sweep_dist_obj = NormalSchema().load(sweep_distribution)  # pylint: disable=no-member
+        elif sweep_dist_type in SearchSpace.QUNIFORM_QLOGUNIFORM:
+            sweep_dist_obj = QUniformSchema().load(sweep_distribution)  # pylint: disable=no-member
+        elif sweep_dist_type in SearchSpace.QNORMAL_QLOGNORMAL:
+            sweep_dist_obj = QNormalSchema().load(sweep_distribution)  # pylint: disable=no-member
+        elif sweep_dist_type in SearchSpace.RANDINT:
+            sweep_dist_obj = RandintSchema().load(sweep_distribution)  # pylint: disable=no-member
+        else:
+            msg = f"Unsupported sweep distribution type {sweep_dist_type}"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+    else:  # Case for other primitive types
+        sweep_dist_obj = sweep_distribution
+
+    sweep_dist_str = _convert_to_rest_object(sweep_dist_obj)
+    return sweep_dist_str
+
+
+def _convert_sweep_dist_str_to_dict(sweep_dist_str_list: dict) -> dict:
+    for k, val in sweep_dist_str_list.items():
+        if isinstance(val, str):
+            sweep_dist_str_list[k] = _convert_sweep_dist_str_item_to_dict(val)
+    return sweep_dist_str_list
+
+
+def _convert_sweep_dist_str_item_to_dict(
+    sweep_distribution_str: str,
+) -> Union[bool, int, float, str, dict]:
+    # sweep_distribution_str can be a distribution like "choice('vitb16r224', 'vits16r224')"
+    # return type is {type: 'choice', values: ['vitb16r224', 'vits16r224']}
+    sweep_dist_obj = _convert_from_rest_object(sweep_distribution_str)
+    sweep_dist: Union[bool, int, float, str, dict] = ""
+    if isinstance(sweep_dist_obj, SweepDistribution):
+        if isinstance(sweep_dist_obj, Choice):
+            sweep_dist = ChoicePlusSchema().dump(sweep_dist_obj)  # pylint: disable=no-member
+        elif isinstance(sweep_dist_obj, (QNormal, QLogNormal)):
+            sweep_dist = QNormalSchema().dump(sweep_dist_obj)  # pylint: disable=no-member
+        elif isinstance(sweep_dist_obj, (QUniform, QLogUniform)):
+            sweep_dist = QUniformSchema().dump(sweep_dist_obj)  # pylint: disable=no-member
+        elif isinstance(sweep_dist_obj, (Uniform, LogUniform)):
+            sweep_dist = UniformSchema().dump(sweep_dist_obj)  # pylint: disable=no-member
+        elif isinstance(sweep_dist_obj, (Normal, LogNormal)):
+            sweep_dist = NormalSchema().dump(sweep_dist_obj)  # pylint: disable=no-member
+        elif isinstance(sweep_dist_obj, Randint):
+            sweep_dist = RandintSchema().dump(sweep_dist_obj)  # pylint: disable=no-member
+        else:
+            msg = "Invalid sweep distribution {}"
+            raise ValidationException(
+                message=msg.format(sweep_distribution_str),
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+    else:  # Case for other primitive types
+        sweep_dist = sweep_dist_obj
+
+    return sweep_dist
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/stack_ensemble_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/stack_ensemble_settings.py
new file mode 100644
index 00000000..c17fa7e3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/stack_ensemble_settings.py
@@ -0,0 +1,70 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Any, Optional
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import StackEnsembleSettings as RestStackEnsembleSettings
+from azure.ai.ml._restclient.v2023_04_01_preview.models import StackMetaLearnerType
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class StackEnsembleSettings(RestTranslatableMixin):
+    """Advance setting to customize StackEnsemble run."""
+
+    def __init__(
+        self,
+        *,
+        stack_meta_learner_k_wargs: Optional[Any] = None,
+        stack_meta_learner_train_percentage: float = 0.2,
+        stack_meta_learner_type: Optional[StackMetaLearnerType] = None,
+        **kwargs: Any
+    ):
+        """
+        :param stack_meta_learner_k_wargs: Optional parameters to pass to the initializer of the
+         meta-learner.
+        :type stack_meta_learner_k_wargs: any
+        :param stack_meta_learner_train_percentage: Specifies the proportion of the training set
+         (when choosing train and validation type of training) to be reserved for training the
+         meta-learner. Default value is 0.2.
+        :type stack_meta_learner_train_percentage: float
+        :param stack_meta_learner_type: The meta-learner is a model trained on the output of the
+         individual heterogeneous models. Possible values include: "None", "LogisticRegression",
+         "LogisticRegressionCV", "LightGBMClassifier", "ElasticNet", "ElasticNetCV",
+         "LightGBMRegressor", "LinearRegression".
+        :type stack_meta_learner_type: str or
+         ~azure.mgmt.machinelearningservices.models.StackMetaLearnerType
+        """
+        super(StackEnsembleSettings, self).__init__(**kwargs)
+        self.stack_meta_learner_k_wargs = stack_meta_learner_k_wargs
+        self.stack_meta_learner_train_percentage = stack_meta_learner_train_percentage
+        self.stack_meta_learner_type = stack_meta_learner_type
+
+    def _to_rest_object(self) -> RestStackEnsembleSettings:
+        return RestStackEnsembleSettings(
+            stack_meta_learner_k_wargs=self.stack_meta_learner_k_wargs,
+            stack_meta_learner_train_percentage=self.stack_meta_learner_train_percentage,
+            stack_meta_learner_type=self.stack_meta_learner_type,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestStackEnsembleSettings) -> "StackEnsembleSettings":
+        return cls(
+            stack_meta_learner_k_wargs=obj.stack_meta_learner_k_wargs,
+            stack_meta_learner_train_percentage=obj.stack_meta_learner_train_percentage,
+            stack_meta_learner_type=obj.stack_meta_learner_type,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, StackEnsembleSettings):
+            return NotImplemented
+
+        return (
+            super().__eq__(other)
+            and self.stack_meta_learner_k_wargs == other.stack_meta_learner_k_wargs
+            and self.stack_meta_learner_train_percentage == other.stack_meta_learner_train_percentage
+            and self.stack_meta_learner_type == other.stack_meta_learner_type
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/__init__.py
new file mode 100644
index 00000000..c0373010
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/__init__.py
@@ -0,0 +1,22 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from .automl_tabular import AutoMLTabular
+from .classification_job import ClassificationJob
+from .featurization_settings import ColumnTransformer, TabularFeaturizationSettings
+from .forecasting_job import ForecastingJob
+from .forecasting_settings import ForecastingSettings
+from .limit_settings import TabularLimitSettings
+from .regression_job import RegressionJob
+
+__all__ = [
+    "AutoMLTabular",
+    "ClassificationJob",
+    "ColumnTransformer",
+    "ForecastingJob",
+    "ForecastingSettings",
+    "RegressionJob",
+    "TabularFeaturizationSettings",
+    "TabularLimitSettings",
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/automl_tabular.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/automl_tabular.py
new file mode 100644
index 00000000..5f4ed22b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/automl_tabular.py
@@ -0,0 +1,607 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=too-many-instance-attributes
+
+from abc import ABC
+from typing import Any, Dict, List, Optional, Union
+
+from azure.ai.ml._restclient.v2024_01_01_preview.models import (
+    AutoNCrossValidations,
+    BlockedTransformers,
+    CustomNCrossValidations,
+    LogVerbosity,
+)
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.constants import TabularTrainingMode
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job.automl.automl_vertical import AutoMLVertical
+from azure.ai.ml.entities._job.automl.stack_ensemble_settings import StackEnsembleSettings
+from azure.ai.ml.entities._job.automl.tabular.featurization_settings import (
+    ColumnTransformer,
+    TabularFeaturizationSettings,
+)
+from azure.ai.ml.entities._job.automl.tabular.limit_settings import TabularLimitSettings
+from azure.ai.ml.entities._job.automl.training_settings import TrainingSettings
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+class AutoMLTabular(AutoMLVertical, ABC):
+    """Initialize an AutoML job entity for tabular data.
+
+    Constructor for AutoMLTabular.
+
+    :keyword task_type: The type of task to run. Possible values include: "classification", "regression"
+        , "forecasting".
+    :paramtype task_type: str
+    :keyword featurization: featurization settings. Defaults to None.
+    :paramtype featurization: typing.Optional[TabularFeaturizationSettings]
+    :keyword limits: limits settings. Defaults to None.
+    :paramtype limits: typing.Optional[TabularLimitSettings]
+    :keyword training: training settings. Defaults to None.
+    :paramtype training: typing.Optional[TrainingSettings]
+    :keyword log_verbosity: Verbosity of logging. Possible values include: "debug", "info", "warning", "error",
+        "critical". Defaults to "info".
+    :paramtype log_verbosity: str
+    :keyword target_column_name: The name of the target column. Defaults to None.
+    :paramtype target_column_name: typing.Optional[str]
+    :keyword weight_column_name: The name of the weight column. Defaults to None.
+    :paramtype weight_column_name: typing.Optional[str]
+    :keyword validation_data_size: The size of the validation data. Defaults to None.
+    :paramtype validation_data_size: typing.Optional[float]
+    :keyword cv_split_column_names: The names of the columns to use for cross validation. Defaults to None.
+    :paramtype cv_split_column_names: typing.Optional[List[str]]
+    :keyword n_cross_validations: The number of cross validations to run. Defaults to None.
+    :paramtype n_cross_validations: typing.Optional[int]
+    :keyword test_data_size: The size of the test data. Defaults to None.
+    :paramtype test_data_size: typing.Optional[float]
+    :keyword training_data: The training data. Defaults to None.
+    :paramtype training_data: typing.Optional[azure.ai.ml.entities._inputs_outputs.Input]
+    :keyword validation_data: The validation data. Defaults to None.
+    :paramtype validation_data: typing.Optional[azure.ai.ml.entities._inputs_outputs.Input]
+    :keyword test_data: The test data. Defaults to None.
+    :paramtype test_data: typing.Optional[azure.ai.ml.entities._inputs_outputs.Input]
+    """
+
+    def __init__(
+        self,
+        *,
+        task_type: str,
+        featurization: Optional[TabularFeaturizationSettings] = None,
+        limits: Optional[TabularLimitSettings] = None,
+        training: Optional[Any] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize an AutoML job entity for tabular data.
+
+        Constructor for AutoMLTabular.
+
+        :keyword task_type: The type of task to run. Possible values include: "classification", "regression"
+            , "forecasting".
+        :paramtype task_type: str
+        :keyword featurization: featurization settings. Defaults to None.
+        :paramtype featurization: typing.Optional[TabularFeaturizationSettings]
+        :keyword limits: limits settings. Defaults to None.
+        :paramtype limits: typing.Optional[TabularLimitSettings]
+        :keyword training: training settings. Defaults to None.
+        :paramtype training: typing.Optional[TrainingSettings]
+        :keyword log_verbosity: Verbosity of logging. Possible values include: "debug", "info", "warning", "error",
+            "critical". Defaults to "info".
+        :paramtype log_verbosity: str
+        :keyword target_column_name: The name of the target column. Defaults to None.
+        :paramtype target_column_name: typing.Optional[str]
+        :keyword weight_column_name: The name of the weight column. Defaults to None.
+        :paramtype weight_column_name: typing.Optional[str]
+        :keyword validation_data_size: The size of the validation data. Defaults to None.
+        :paramtype validation_data_size: typing.Optional[float]
+        :keyword cv_split_column_names: The names of the columns to use for cross validation. Defaults to None.
+        :paramtype cv_split_column_names: typing.Optional[List[str]]
+        :keyword n_cross_validations: The number of cross validations to run. Defaults to None.
+        :paramtype n_cross_validations: typing.Optional[int]
+        :keyword test_data_size: The size of the test data. Defaults to None.
+        :paramtype test_data_size: typing.Optional[float]
+        :keyword training_data: The training data. Defaults to None.
+        :paramtype training_data: typing.Optional[azure.ai.ml.entities._inputs_outputs.Input]
+        :keyword validation_data: The validation data. Defaults to None.
+        :paramtype validation_data: typing.Optional[azure.ai.ml.entities._inputs_outputs.Input]
+        :keyword test_data: The test data. Defaults to None.
+        :paramtype test_data: typing.Optional[azure.ai.ml.entities._inputs_outputs.Input]
+        :raises: :class:`azure.ai.ml.exceptions.ValidationException`
+        """
+        self.log_verbosity = kwargs.pop("log_verbosity", LogVerbosity.INFO)
+
+        self.target_column_name = kwargs.pop("target_column_name", None)
+        self.weight_column_name = kwargs.pop("weight_column_name", None)
+        self.validation_data_size = kwargs.pop("validation_data_size", None)
+        self.cv_split_column_names = kwargs.pop("cv_split_column_names", None)
+        self.n_cross_validations = kwargs.pop("n_cross_validations", None)
+        self.test_data_size = kwargs.pop("test_data_size", None)
+
+        super().__init__(
+            task_type=task_type,
+            training_data=kwargs.pop("training_data", None),
+            validation_data=kwargs.pop("validation_data", None),
+            test_data=kwargs.pop("test_data", None),
+            **kwargs,
+        )
+
+        self._featurization = featurization
+        self._limits = limits
+        self._training = training
+
+    @property
+    def log_verbosity(self) -> LogVerbosity:
+        """Get the log verbosity for the AutoML job.
+
+        :return: log verbosity for the AutoML job
+        :rtype: LogVerbosity
+        """
+        return self._log_verbosity
+
+    @log_verbosity.setter
+    def log_verbosity(self, value: Union[str, LogVerbosity]) -> None:
+        """Set the log verbosity for the AutoML job.
+
+        :param value: str or LogVerbosity
+        :type value: typing.Union[str, LogVerbosity]
+        """
+        self._log_verbosity = None if value is None else LogVerbosity[camel_to_snake(value).upper()]
+
+    @property
+    def limits(self) -> Optional[TabularLimitSettings]:
+        """Get the tabular limits for the AutoML job.
+
+        :return: Tabular limits for the AutoML job
+        :rtype: TabularLimitSettings
+        """
+        return self._limits
+
+    @limits.setter
+    def limits(self, value: Union[Dict, TabularLimitSettings]) -> None:
+        """Set the limits for the AutoML job.
+
+        :param value: typing.Dict or TabularLimitSettings
+        :type value: typing.Union[typing.Dict, TabularLimitSettings]
+        :raises ValidationException: Expected a dictionary for limit settings.
+        """
+        if isinstance(value, TabularLimitSettings):
+            self._limits = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for limit settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_limits(**value)
+
+    @property
+    def training(self) -> Any:
+        """Get the training settings for the AutoML job.
+
+        :return: Training settings for the AutoML job.
+        :rtype: TrainingSettings
+        """
+        return self._training
+
+    @training.setter
+    def training(self, value: Union[Dict, TrainingSettings]) -> None:
+        """Set the training settings for the AutoML job.
+
+        :param value: typing.Dict or TrainingSettings
+        :type value: typing.Union[typing.Dict, TrainingSettings]
+        :raises ValidationException: Expected a dictionary for training settings.
+        """
+        if isinstance(value, TrainingSettings):
+            self._training = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for training settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_training(**value)
+
+    @property
+    def featurization(self) -> Optional[TabularFeaturizationSettings]:
+        """Get the tabular featurization settings for the AutoML job.
+
+        :return: Tabular featurization settings for the AutoML job
+        :rtype: TabularFeaturizationSettings
+        """
+        return self._featurization
+
+    @featurization.setter
+    def featurization(self, value: Union[Dict, TabularFeaturizationSettings]) -> None:
+        """Set the featurization settings for the AutoML job.
+
+        :param value: typing.Dict or TabularFeaturizationSettings
+        :type value: typing.Union[typing.Dict, TabularFeaturizationSettings]
+        :raises ValidationException: Expected a dictionary for featurization settings
+        """
+        if isinstance(value, TabularFeaturizationSettings):
+            self._featurization = value
+        else:
+            if not isinstance(value, dict):
+                msg = "Expected a dictionary for featurization settings."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            self.set_featurization(**value)
+
+    def set_limits(
+        self,
+        *,
+        enable_early_termination: Optional[bool] = None,
+        exit_score: Optional[float] = None,
+        max_concurrent_trials: Optional[int] = None,
+        max_cores_per_trial: Optional[int] = None,
+        max_nodes: Optional[int] = None,
+        max_trials: Optional[int] = None,
+        timeout_minutes: Optional[int] = None,
+        trial_timeout_minutes: Optional[int] = None,
+    ) -> None:
+        """Set limits for the job.
+
+        :keyword enable_early_termination: Whether to enable early termination if the score is not improving in the
+            short term, defaults to None.
+
+            Early stopping logic:
+
+            * No early stopping for first 20 iterations (landmarks).
+            * Early stopping window starts on the 21st iteration and looks for early_stopping_n_iters iterations
+                (currently set to 10). This means that the first iteration where stopping can occur is the 31st.
+            * AutoML still schedules 2 ensemble iterations AFTER early stopping, which might result in higher scores.
+            * Early stopping is triggered if the absolute value of best score calculated is the same for past
+                early_stopping_n_iters iterations, that is, if there is no improvement in score for
+                early_stopping_n_iters iterations.
+
+        :paramtype enable_early_termination: typing.Optional[bool]
+        :keyword exit_score: Target score for experiment. The experiment terminates after this score is reached.
+            If not specified (no criteria), the experiment runs until no further progress is made
+            on the primary metric. For for more information on exit criteria, see this `article
+            <https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#exit-criteria>`_
+            , defaults to None
+        :paramtype exit_score: typing.Optional[float]
+        :keyword max_concurrent_trials: This is the maximum number of iterations that would be executed in parallel.
+            The default value is 1.
+
+            * AmlCompute clusters support one iteration running per node. For multiple AutoML experiment parent runs
+                executed in parallel on a single AmlCompute cluster, the sum of the ``max_concurrent_trials`` values
+                for all experiments should be less than or equal to the maximum number of nodes. Otherwise, runs
+                will be queued until nodes are available.
+
+            * DSVM supports multiple iterations per node. ``max_concurrent_trials`` should
+                be less than or equal to the number of cores on the DSVM. For multiple experiments
+                run in parallel on a single DSVM, the sum of the ``max_concurrent_trials`` values for all
+                experiments should be less than or equal to the maximum number of nodes.
+
+            * Databricks - ``max_concurrent_trials`` should be less than or equal to the number of
+                worker nodes on Databricks.
+
+            ``max_concurrent_trials`` does not apply to local runs. Formerly, this parameter
+            was named ``concurrent_iterations``.
+        :paramtype max_concurrent_trials: typing.Optional[int]
+        :keyword max_cores_per_trial: The maximum number of threads to use for a given training iteration.
+            Acceptable values:
+
+            * Greater than 1 and less than or equal to the maximum number of cores on the compute target.
+
+            * Equal to -1, which means to use all the possible cores per iteration per child-run.
+
+            * Equal to 1, the default.
+
+        :paramtype max_cores_per_trial: typing.Optional[int]
+        :keyword max_nodes: [Experimental] The maximum number of nodes to use for distributed training.
+
+            * For forecasting, each model is trained using max(2, int(max_nodes / max_concurrent_trials)) nodes.
+
+            * For classification/regression, each model is trained using max_nodes nodes.
+
+            Note- This parameter is in public preview and might change in future.
+        :paramtype max_nodes: typing.Optional[int]
+        :keyword max_trials: The total number of different algorithm and parameter combinations to test during an
+            automated ML experiment. If not specified, the default is 1000 iterations.
+        :paramtype max_trials: typing.Optional[int]
+        :keyword timeout_minutes:  Maximum amount of time in minutes that all iterations combined can take before the
+            experiment terminates. If not specified, the default experiment timeout is 6 days. To specify a timeout
+            less than or equal to 1 hour, make sure your dataset's size is not greater than
+            10,000,000 (rows times column) or an error results, defaults to None
+        :paramtype timeout_minutes: typing.Optional[int]
+        :keyword trial_timeout_minutes: Maximum time in minutes that each iteration can run for before it terminates.
+            If not specified, a value of 1 month or 43200 minutes is used, defaults to None
+        :paramtype trial_timeout_minutes: typing.Optional[int]
+        """
+        self._limits = self._limits or TabularLimitSettings()
+        self._limits.enable_early_termination = (
+            enable_early_termination if enable_early_termination is not None else self._limits.enable_early_termination
+        )
+        self._limits.exit_score = exit_score if exit_score is not None else self._limits.exit_score
+        self._limits.max_concurrent_trials = (
+            max_concurrent_trials if max_concurrent_trials is not None else self._limits.max_concurrent_trials
+        )
+        self._limits.max_cores_per_trial = (
+            max_cores_per_trial if max_cores_per_trial is not None else self._limits.max_cores_per_trial
+        )
+        self._limits.max_nodes = max_nodes if max_nodes is not None else self._limits.max_nodes
+        self._limits.max_trials = max_trials if max_trials is not None else self._limits.max_trials
+        self._limits.timeout_minutes = timeout_minutes if timeout_minutes is not None else self._limits.timeout_minutes
+        self._limits.trial_timeout_minutes = (
+            trial_timeout_minutes if trial_timeout_minutes is not None else self._limits.trial_timeout_minutes
+        )
+
+    def set_training(
+        self,
+        *,
+        enable_onnx_compatible_models: Optional[bool] = None,
+        enable_dnn_training: Optional[bool] = None,
+        enable_model_explainability: Optional[bool] = None,
+        enable_stack_ensemble: Optional[bool] = None,
+        enable_vote_ensemble: Optional[bool] = None,
+        stack_ensemble_settings: Optional[StackEnsembleSettings] = None,
+        ensemble_model_download_timeout: Optional[int] = None,
+        allowed_training_algorithms: Optional[List[str]] = None,
+        blocked_training_algorithms: Optional[List[str]] = None,
+        training_mode: Optional[Union[str, TabularTrainingMode]] = None,
+    ) -> None:
+        """The method to configure training related settings.
+
+        :keyword enable_onnx_compatible_models: Whether to enable or disable enforcing the ONNX-compatible models.
+            The default is False. For more information about Open Neural Network Exchange (ONNX) and Azure Machine
+            Learning,see this `article <https://learn.microsoft.com/azure/machine-learning/concept-onnx>`__.
+        :paramtype enable_onnx_compatible_models: typing.Optional[bool]
+        :keyword enable_dnn_training: Whether to include DNN based models during model selection.
+            However, the default is True for DNN NLP tasks, and it's False for all other AutoML tasks.
+        :paramtype enable_dnn_training: typing.Optional[bool]
+        :keyword enable_model_explainability: Whether to enable explaining the best AutoML model at the end of all
+            AutoML training iterations. For more information, see
+            `Interpretability: model explanations in automated machine learning
+            <https://learn.microsoft.com/azure/machine-learning/how-to-machine-learning-interpretability-automl>`__.
+            , defaults to None
+        :paramtype enable_model_explainability: typing.Optional[bool]
+        :keyword enable_stack_ensemble: Whether to enable/disable StackEnsemble iteration.
+            If `enable_onnx_compatible_models` flag is being set, then StackEnsemble iteration will be disabled.
+            Similarly, for Timeseries tasks, StackEnsemble iteration will be disabled by default, to avoid risks of
+            overfitting due to small training set used in fitting the meta learner.
+            For more information about ensembles, see `Ensemble configuration
+            <https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#ensemble>`__
+            , defaults to None
+        :paramtype enable_stack_ensemble: typing.Optional[bool]
+        :keyword enable_vote_ensemble: Whether to enable/disable VotingEnsemble iteration.
+            For more information about ensembles, see `Ensemble configuration
+            <https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#ensemble>`__
+            , defaults to None
+        :paramtype enable_vote_ensemble: typing.Optional[bool]
+        :keyword stack_ensemble_settings: Settings for StackEnsemble iteration, defaults to None
+        :paramtype stack_ensemble_settings: typing.Optional[StackEnsembleSettings]
+        :keyword ensemble_model_download_timeout: During VotingEnsemble and StackEnsemble model generation,
+            multiple fitted models from the previous child runs are downloaded. Configure this parameter with a
+            higher value than 300 secs, if more time is needed, defaults to None
+        :paramtype ensemble_model_download_timeout: typing.Optional[int]
+        :keyword allowed_training_algorithms: A list of model names to search for an experiment. If not specified,
+            then all models supported for the task are used minus any specified in ``blocked_training_algorithms``
+            or deprecated TensorFlow models, defaults to None
+        :paramtype allowed_training_algorithms: typing.Optional[List[str]]
+        :keyword blocked_training_algorithms: A list of algorithms to ignore for an experiment, defaults to None
+        :paramtype blocked_training_algorithms: typing.Optional[List[str]]
+        :keyword training_mode: [Experimental] The training mode to use.
+            The possible values are-
+
+            * distributed- enables distributed training for supported algorithms.
+
+            * non_distributed- disables distributed training.
+
+            * auto- Currently, it is same as non_distributed. In future, this might change.
+
+            Note: This parameter is in public preview and may change in future.
+        :paramtype training_mode: typing.Optional[typing.Union[str, azure.ai.ml.constants.TabularTrainingMode]]
+        """
+        # get training object by calling training getter of respective tabular task
+        self._training = self.training
+        if self._training is not None:
+            self._training.enable_onnx_compatible_models = (
+                enable_onnx_compatible_models
+                if enable_onnx_compatible_models is not None
+                else self._training.enable_onnx_compatible_models
+            )
+            self._training.enable_dnn_training = (
+                enable_dnn_training if enable_dnn_training is not None else self._training.enable_dnn_training
+            )
+            self._training.enable_model_explainability = (
+                enable_model_explainability
+                if enable_model_explainability is not None
+                else self._training.enable_model_explainability
+            )
+            self._training.enable_stack_ensemble = (
+                enable_stack_ensemble if enable_stack_ensemble is not None else self._training.enable_stack_ensemble
+            )
+            self._training.enable_vote_ensemble = (
+                enable_vote_ensemble if enable_vote_ensemble is not None else self._training.enable_vote_ensemble
+            )
+            self._training.stack_ensemble_settings = (
+                stack_ensemble_settings
+                if stack_ensemble_settings is not None
+                else self._training.stack_ensemble_settings
+            )
+            self._training.ensemble_model_download_timeout = (
+                ensemble_model_download_timeout
+                if ensemble_model_download_timeout is not None
+                else self._training.ensemble_model_download_timeout
+            )
+
+            self._training.allowed_training_algorithms = allowed_training_algorithms
+            self._training.blocked_training_algorithms = blocked_training_algorithms
+            self._training.training_mode = training_mode if training_mode is not None else self._training.training_mode
+
+    def set_featurization(
+        self,
+        *,
+        blocked_transformers: Optional[List[Union[BlockedTransformers, str]]] = None,
+        column_name_and_types: Optional[Dict[str, str]] = None,
+        dataset_language: Optional[str] = None,
+        transformer_params: Optional[Dict[str, List[ColumnTransformer]]] = None,
+        mode: Optional[str] = None,
+        enable_dnn_featurization: Optional[bool] = None,
+    ) -> None:
+        """Define feature engineering configuration.
+
+        :keyword blocked_transformers: A list of transformer names to be blocked during featurization, defaults to None
+        :paramtype blocked_transformers: Optional[List[Union[BlockedTransformers, str]]]
+        :keyword column_name_and_types: A dictionary of column names and feature types used to update column purpose
+            , defaults to None
+        :paramtype column_name_and_types: Optional[Dict[str, str]]
+        :keyword dataset_language: Three character ISO 639-3 code for the language(s) contained in the dataset.
+            Languages other than English are only supported if you use GPU-enabled compute.  The language_code
+            'mul' should be used if the dataset contains multiple languages. To find ISO 639-3 codes for different
+            languages, please refer to https://en.wikipedia.org/wiki/List_of_ISO_639-3_codes, defaults to None
+        :paramtype dataset_language: Optional[str]
+        :keyword transformer_params: A dictionary of transformer and corresponding customization parameters
+            , defaults to None
+        :paramtype transformer_params: Optional[Dict[str, List[ColumnTransformer]]]
+        :keyword mode: "off", "auto", defaults to "auto", defaults to None
+        :paramtype mode: Optional[str]
+        :keyword enable_dnn_featurization: Whether to include DNN based feature engineering methods, defaults to None
+        :paramtype enable_dnn_featurization: Optional[bool]
+        """
+        self._featurization = self._featurization or TabularFeaturizationSettings()
+        self._featurization.blocked_transformers = (
+            blocked_transformers if blocked_transformers is not None else self._featurization.blocked_transformers
+        )
+        self._featurization.column_name_and_types = (
+            column_name_and_types if column_name_and_types is not None else self._featurization.column_name_and_types
+        )
+        self._featurization.dataset_language = (
+            dataset_language if dataset_language is not None else self._featurization.dataset_language
+        )
+        self._featurization.transformer_params = (
+            transformer_params if transformer_params is not None else self._featurization.transformer_params
+        )
+        self._featurization.mode = mode or self._featurization.mode
+        self._featurization.enable_dnn_featurization = (
+            enable_dnn_featurization
+            if enable_dnn_featurization is not None
+            else self._featurization.enable_dnn_featurization
+        )
+
+    def set_data(
+        self,
+        *,
+        training_data: Input,
+        target_column_name: str,
+        weight_column_name: Optional[str] = None,
+        validation_data: Optional[Input] = None,
+        validation_data_size: Optional[float] = None,
+        n_cross_validations: Optional[Union[str, int]] = None,
+        cv_split_column_names: Optional[List[str]] = None,
+        test_data: Optional[Input] = None,
+        test_data_size: Optional[float] = None,
+    ) -> None:
+        """Define data configuration.
+
+        :keyword training_data: Training data.
+        :paramtype training_data: Input
+        :keyword target_column_name: Column name of the target column.
+        :paramtype target_column_name: str
+        :keyword weight_column_name: Weight column name, defaults to None
+        :paramtype weight_column_name: typing.Optional[str]
+        :keyword validation_data: Validation data, defaults to None
+        :paramtype validation_data: typing.Optional[Input]
+        :keyword validation_data_size: Validation data size, defaults to None
+        :paramtype validation_data_size: typing.Optional[float]
+        :keyword n_cross_validations: n_cross_validations, defaults to None
+        :paramtype n_cross_validations: typing.Optional[typing.Union[str, int]]
+        :keyword cv_split_column_names: cv_split_column_names, defaults to None
+        :paramtype cv_split_column_names: typing.Optional[typing.List[str]]
+        :keyword test_data: Test data, defaults to None
+        :paramtype test_data: typing.Optional[Input]
+        :keyword test_data_size: Test data size, defaults to None
+        :paramtype test_data_size: typing.Optional[float]
+        """
+        self.target_column_name = target_column_name if target_column_name is not None else self.target_column_name
+        self.weight_column_name = weight_column_name if weight_column_name is not None else self.weight_column_name
+        self.training_data = training_data if training_data is not None else self.training_data
+        self.validation_data = validation_data if validation_data is not None else self.validation_data
+        self.validation_data_size = (
+            validation_data_size if validation_data_size is not None else self.validation_data_size
+        )
+        self.cv_split_column_names = (
+            cv_split_column_names if cv_split_column_names is not None else self.cv_split_column_names
+        )
+        self.n_cross_validations = n_cross_validations if n_cross_validations is not None else self.n_cross_validations
+        self.test_data = test_data if test_data is not None else self.test_data
+        self.test_data_size = test_data_size if test_data_size is not None else self.test_data_size
+
+    def _validation_data_to_rest(self, rest_obj: "AutoMLTabular") -> None:
+        """Validation data serialization.
+
+        :param rest_obj: Serialized object
+        :type rest_obj: AutoMLTabular
+        """
+        if rest_obj.n_cross_validations:
+            n_cross_val = rest_obj.n_cross_validations
+            # Convert n_cross_validations int value to CustomNCrossValidations
+            if isinstance(n_cross_val, int) and n_cross_val > 1:
+                rest_obj.n_cross_validations = CustomNCrossValidations(value=n_cross_val)
+            # Convert n_cross_validations str value to AutoNCrossValidations
+            elif isinstance(n_cross_val, str):
+                rest_obj.n_cross_validations = AutoNCrossValidations()
+
+    def _validation_data_from_rest(self) -> None:
+        """Validation data deserialization."""
+        if self.n_cross_validations:
+            n_cross_val = self.n_cross_validations
+            # Convert n_cross_validations CustomNCrossValidations back into int value
+            if isinstance(n_cross_val, CustomNCrossValidations):
+                self.n_cross_validations = n_cross_val.value
+            # Convert n_cross_validations AutoNCrossValidations to str value
+            elif isinstance(n_cross_val, AutoNCrossValidations):
+                self.n_cross_validations = AutoMLConstants.AUTO
+
+    def __eq__(self, other: object) -> bool:
+        """Return True if both instances have the same values.
+
+        This method check instances equality and returns True if both of
+            the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, AutoMLTabular):
+            return NotImplemented
+
+        return (
+            self.target_column_name == other.target_column_name
+            and self.weight_column_name == other.weight_column_name
+            and self.training_data == other.training_data
+            and self.validation_data == other.validation_data
+            and self.validation_data_size == other.validation_data_size
+            and self.cv_split_column_names == other.cv_split_column_names
+            and self.n_cross_validations == other.n_cross_validations
+            and self.test_data == other.test_data
+            and self.test_data_size == other.test_data_size
+            and self._featurization == other._featurization
+            and self._limits == other._limits
+            and self._training == other._training
+        )
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two AutoMLTabular objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/classification_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/classification_job.py
new file mode 100644
index 00000000..6f5ab271
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/classification_job.py
@@ -0,0 +1,352 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Classification as RestClassification
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ClassificationPrimaryMetrics, JobBase, TaskType
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.tabular.automl_tabular import AutoMLTabular
+from azure.ai.ml.entities._job.automl.tabular.featurization_settings import TabularFeaturizationSettings
+from azure.ai.ml.entities._job.automl.tabular.limit_settings import TabularLimitSettings
+from azure.ai.ml.entities._job.automl.training_settings import (  # noqa: F401 # pylint: disable=unused-import
+    ClassificationTrainingSettings,
+    TrainingSettings,
+)
+from azure.ai.ml.entities._util import load_from_dict
+
+
+class ClassificationJob(AutoMLTabular):
+    """Configuration for AutoML Classification Job.
+
+    :keyword primary_metric: The primary metric to use for optimization, defaults to None
+    :paramtype primary_metric: typing.Optional[str]
+    :keyword positive_label: Positive label for binary metrics calculation, defaults to None
+    :paramtype positive_label: typing.Optional[str]
+    :keyword featurization: Featurization settings. Defaults to None.
+    :paramtype featurization: typing.Optional[TabularFeaturizationSettings]
+    :keyword limits: Limits settings. Defaults to None.
+    :paramtype limits: typing.Optional[TabularLimitSettings]
+    :keyword training: Training settings. Defaults to None.
+    :paramtype training: typing.Optional[TrainingSettings]
+    :return: An instance of ClassificationJob object.
+    :rtype: ~azure.ai.ml.entities.automl.ClassificationJob
+    :raises ValueError: If primary_metric is not a valid primary metric
+    :raises ValueError: If positive_label is not a valid positive label
+    :raises ValueError: If featurization is not a valid featurization settings
+    :raises ValueError: If limits is not a valid limits settings
+    :raises ValueError: If training is not a valid training settings
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ClassificationPrimaryMetrics.ACCURACY
+
+    def __init__(
+        self,
+        *,
+        primary_metric: Optional[str] = None,
+        positive_label: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a new AutoML Classification task.
+
+        :keyword primary_metric: The primary metric to use for optimization, defaults to None
+        :paramtype primary_metric: typing.Optional[str]
+        :keyword positive_label: Positive label for binary metrics calculation, defaults to None
+        :paramtype positive_label: typing.Optional[str]
+        :keyword featurization: featurization settings. Defaults to None.
+        :paramtype featurization: typing.Optional[TabularFeaturizationSettings]
+        :keyword limits: limits settings. Defaults to None.
+        :paramtype limits: typing.Optional[TabularLimitSettings]
+        :keyword training: training settings. Defaults to None.
+        :paramtype training: typing.Optional[TrainingSettings]
+        :raises ValueError: If primary_metric is not a valid primary metric
+        :raises ValueError: If positive_label is not a valid positive label
+        :raises ValueError: If featurization is not a valid featurization settings
+        :raises ValueError: If limits is not a valid limits settings
+        :raises ValueError: If training is not a valid training settings
+        """
+        # Extract any task specific settings
+        featurization = kwargs.pop("featurization", None)
+        limits = kwargs.pop("limits", None)
+        training = kwargs.pop("training", None)
+
+        super().__init__(
+            task_type=TaskType.CLASSIFICATION,
+            featurization=featurization,
+            limits=limits,
+            training=training,
+            **kwargs,
+        )
+
+        self.primary_metric = primary_metric or ClassificationJob._DEFAULT_PRIMARY_METRIC
+        self.positive_label = positive_label
+
+    @property
+    def primary_metric(self) -> Union[str, ClassificationPrimaryMetrics]:
+        """The primary metric to use for optimization.
+
+        :return: The primary metric to use for optimization.
+        :rtype: typing.Union[str, ClassificationPrimaryMetrics]
+        """
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ClassificationPrimaryMetrics]) -> None:
+        """The primary metric to use for optimization setter.
+
+        :param value: Primary metric to use for optimization.
+        :type value: typing.Union[str, ClassificationPrimaryMetrics]
+        """
+        # TODO: better way to do this
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+        self._primary_metric = (
+            ClassificationJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ClassificationPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    @property  # type: ignore
+    def training(self) -> ClassificationTrainingSettings:
+        """Training Settings for AutoML Classification Job.
+
+        :return: Training settings used for AutoML Classification Job.
+        :rtype: ClassificationTrainingSettings
+        """
+        return self._training or ClassificationTrainingSettings()
+
+    @training.setter
+    def training(self, value: Union[Dict, ClassificationTrainingSettings]) -> None:  # pylint: disable=unused-argument
+        ...
+
+    def _to_rest_object(self) -> JobBase:
+        """Convert ClassificationJob object to a REST object.
+
+        :return: REST object representation of this object.
+        :rtype: JobBase
+        """
+        classification_task = RestClassification(
+            target_column_name=self.target_column_name,
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            validation_data_size=self.validation_data_size,
+            weight_column_name=self.weight_column_name,
+            cv_split_column_names=self.cv_split_column_names,
+            n_cross_validations=self.n_cross_validations,
+            test_data=self.test_data,
+            test_data_size=self.test_data_size,
+            featurization_settings=self._featurization._to_rest_object() if self._featurization else None,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            training_settings=self._training._to_rest_object() if self._training else None,
+            primary_metric=self.primary_metric,
+            positive_label=self.positive_label,
+            log_verbosity=self.log_verbosity,
+        )
+        self._resolve_data_inputs(classification_task)
+        self._validation_data_to_rest(classification_task)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=classification_task,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "ClassificationJob":
+        """Convert a REST object to ClassificationJob object.
+
+        :param obj: ClassificationJob in Rest format.
+        :type obj: JobBase
+        :return: ClassificationJob objects.
+        :rtype: ClassificationJob
+        """
+
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestClassification = properties.task_details
+
+        job_args_dict = {
+            "id": obj.id,
+            "name": obj.name,
+            "description": properties.description,
+            "tags": properties.tags,
+            "properties": properties.properties,
+            "experiment_name": properties.experiment_name,
+            "services": properties.services,
+            "status": properties.status,
+            "creation_context": obj.system_data,
+            "display_name": properties.display_name,
+            "compute": properties.compute_id,
+            "outputs": from_rest_data_outputs(properties.outputs),
+            "resources": properties.resources,
+            "identity": (
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            "queue_settings": properties.queue_settings,
+        }
+
+        classification_job = cls(
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            validation_data_size=task_details.validation_data_size,
+            weight_column_name=task_details.weight_column_name,
+            cv_split_column_names=task_details.cv_split_column_names,
+            n_cross_validations=task_details.n_cross_validations,
+            test_data=task_details.test_data,
+            test_data_size=task_details.test_data_size,
+            featurization=(
+                TabularFeaturizationSettings._from_rest_object(task_details.featurization_settings)
+                if task_details.featurization_settings
+                else None
+            ),
+            limits=(
+                TabularLimitSettings._from_rest_object(task_details.limit_settings)
+                if task_details.limit_settings
+                else None
+            ),
+            training=(
+                ClassificationTrainingSettings._from_rest_object(task_details.training_settings)
+                if task_details.training_settings
+                else None
+            ),
+            primary_metric=task_details.primary_metric,
+            positive_label=task_details.positive_label,
+            log_verbosity=task_details.log_verbosity,
+            **job_args_dict,
+        )
+
+        classification_job._restore_data_inputs()
+        classification_job._validation_data_from_rest()
+
+        return classification_job
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "ClassificationJob":
+        """Load from a dictionary.
+
+        :param data: dictionary representation of the object.
+        :type data: typing.Dict
+        :param context: dictionary containing the context.
+        :type context: typing.Dict
+        :param additional_message: additional message to be added to the error message.
+        :type additional_message: str
+        :return: ClassificationJob object.
+        :rtype: ClassificationJob
+        """
+        from azure.ai.ml._schema.automl.table_vertical.classification import AutoMLClassificationSchema
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLClassificationNodeSchema
+
+        if kwargs.pop("inside_pipeline", False):
+            loaded_data = load_from_dict(
+                AutoMLClassificationNodeSchema,
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+        else:
+            loaded_data = load_from_dict(AutoMLClassificationSchema, data, context, additional_message, **kwargs)
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "ClassificationJob":
+        """Create an instance from a schema dictionary.
+
+        :param loaded_data: dictionary containing the data.
+        :type loaded_data: typing.Dict
+        :return: ClassificationJob object.
+        :rtype: ClassificationJob
+        """
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        data_settings = {
+            "training_data": loaded_data.pop("training_data"),
+            "target_column_name": loaded_data.pop("target_column_name"),
+            "weight_column_name": loaded_data.pop("weight_column_name", None),
+            "validation_data": loaded_data.pop("validation_data", None),
+            "validation_data_size": loaded_data.pop("validation_data_size", None),
+            "cv_split_column_names": loaded_data.pop("cv_split_column_names", None),
+            "n_cross_validations": loaded_data.pop("n_cross_validations", None),
+            "test_data": loaded_data.pop("test_data", None),
+            "test_data_size": loaded_data.pop("test_data_size", None),
+        }
+        job = ClassificationJob(**loaded_data)
+        job.set_data(**data_settings)
+        return job
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        """Convert the object to a dictionary.
+
+        :param inside_pipeline: whether the job is inside a pipeline or not, defaults to False
+        :type inside_pipeline: bool
+        :return: dictionary representation of the object.
+        :rtype: typing.Dict
+        """
+        from azure.ai.ml._schema.automl.table_vertical.classification import AutoMLClassificationSchema
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLClassificationNodeSchema
+
+        schema_dict: dict = {}
+        if inside_pipeline:
+            schema_dict = AutoMLClassificationNodeSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        else:
+            schema_dict = AutoMLClassificationSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+
+        return schema_dict
+
+    def __eq__(self, other: object) -> bool:
+        """Returns True if both instances have the same values.
+
+        This method check instances equality and returns True if both of
+            the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, ClassificationJob):
+            return NotImplemented
+
+        if not super().__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two ImageLimitSettings objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/featurization_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/featurization_settings.py
new file mode 100644
index 00000000..6ef2332e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/featurization_settings.py
@@ -0,0 +1,170 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import logging
+from typing import Dict, List, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import BlockedTransformers
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ColumnTransformer as RestColumnTransformer
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    TableVerticalFeaturizationSettings as RestTabularFeaturizationSettings,
+)
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.constants._job.automl import AutoMLTransformerParameterKeys
+from azure.ai.ml.entities._job.automl.featurization_settings import FeaturizationSettings, FeaturizationSettingsType
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+module_logger = logging.getLogger(__name__)
+
+
+class ColumnTransformer(RestTranslatableMixin):
+    """Column transformer settings.
+
+    :param fields: The fields on which to perform custom featurization
+    :type field: List[str]
+    :param parameters: parameters used for custom featurization
+    :type parameters: Dict[str, Optional[str, float]]
+    """
+
+    def __init__(
+        self,
+        *,
+        fields: Optional[List[str]] = None,
+        parameters: Optional[Dict[str, Union[str, float]]] = None,
+    ):
+        self.fields = fields
+        self.parameters = parameters
+
+    def _to_rest_object(self) -> RestColumnTransformer:
+        return RestColumnTransformer(fields=self.fields, parameters=self.parameters)
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestColumnTransformer) -> Optional["ColumnTransformer"]:
+        if obj:
+            fields = obj.fields
+            parameters = obj.parameters
+            return ColumnTransformer(fields=fields, parameters=parameters)
+        return None
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ColumnTransformer):
+            return NotImplemented
+        return self.fields == other.fields and self.parameters == other.parameters
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
+
+
+class TabularFeaturizationSettings(FeaturizationSettings):
+    """Featurization settings for an AutoML Job."""
+
+    def __init__(
+        self,
+        *,
+        blocked_transformers: Optional[List[Union[BlockedTransformers, str]]] = None,
+        column_name_and_types: Optional[Dict[str, str]] = None,
+        dataset_language: Optional[str] = None,
+        transformer_params: Optional[Dict[str, List[ColumnTransformer]]] = None,
+        mode: Optional[str] = None,
+        enable_dnn_featurization: Optional[bool] = None,
+    ):
+        """
+        :param blocked_transformers: A list of transformers to ignore when featurizing.
+        :type blocked_transformers: List[Union[BlockedTransformers, str]]
+        :param column_name_and_types: A dictionary of column names and feature types used to update column purpose.
+        :type column_name_and_types: Dict[str, str]
+        :param dataset_language: The language of the dataset.
+        :type dataset_language: str
+        :param transformer_params: A dictionary of transformers and their parameters.
+        :type transformer_params: Dict[str, List[ColumnTransformer]]
+        :param mode: The mode of the featurization.
+        :type mode: str
+        :param enable_dnn_featurization: Whether to enable DNN featurization.
+        :type enable_dnn_featurization: bool
+        :ivar type: Specifies the type of FeaturizationSettings. Set automatically to "Tabular" for this class.
+        :vartype type: str
+        """
+        super().__init__(dataset_language=dataset_language)
+        self.blocked_transformers = blocked_transformers
+        self.column_name_and_types = column_name_and_types
+        self.transformer_params = transformer_params
+        self.mode = mode
+        self.enable_dnn_featurization = enable_dnn_featurization
+        self.type = FeaturizationSettingsType.TABULAR
+
+    @property
+    def transformer_params(self) -> Optional[Dict[str, List[ColumnTransformer]]]:
+        """A dictionary of transformers and their parameters."""
+        return self._transformer_params
+
+    @transformer_params.setter
+    def transformer_params(self, value: Dict[str, List[ColumnTransformer]]) -> None:
+        self._transformer_params = (
+            None
+            if not value
+            else {(AutoMLTransformerParameterKeys[camel_to_snake(k).upper()].value): v for k, v in value.items()}
+        )
+
+    @property
+    def blocked_transformers(self) -> Optional[List[Union[BlockedTransformers, str]]]:
+        """A list of transformers to ignore when featurizing."""
+        return self._blocked_transformers
+
+    @blocked_transformers.setter
+    def blocked_transformers(self, blocked_transformers_list: List[Union[BlockedTransformers, str]]) -> None:
+        self._blocked_transformers = (
+            None
+            if blocked_transformers_list is None
+            else [BlockedTransformers[camel_to_snake(o)] for o in blocked_transformers_list]
+        )
+
+    def _to_rest_object(self) -> RestTabularFeaturizationSettings:
+        transformer_dict = {}
+        if self.transformer_params:
+            for key, settings in self.transformer_params.items():
+                transformer_dict[key] = [o._to_rest_object() for o in settings]
+        return RestTabularFeaturizationSettings(
+            blocked_transformers=self.blocked_transformers,
+            column_name_and_types=self.column_name_and_types,
+            dataset_language=self.dataset_language,
+            mode=self.mode,
+            transformer_params=transformer_dict,
+            enable_dnn_featurization=self.enable_dnn_featurization,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestTabularFeaturizationSettings) -> "TabularFeaturizationSettings":
+        rest_transformers_params = obj.transformer_params
+        transformer_dict: Optional[Dict] = None
+        if rest_transformers_params:
+            transformer_dict = {}
+            for key, settings in rest_transformers_params.items():
+                transformer_dict[key] = [ColumnTransformer._from_rest_object(o) for o in settings]
+        transformer_params = transformer_dict
+
+        return TabularFeaturizationSettings(
+            blocked_transformers=obj.blocked_transformers,
+            column_name_and_types=obj.column_name_and_types,
+            dataset_language=obj.dataset_language,
+            transformer_params=transformer_params,
+            mode=obj.mode,
+            enable_dnn_featurization=obj.enable_dnn_featurization,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, TabularFeaturizationSettings):
+            return NotImplemented
+        return (
+            super().__eq__(other)
+            and self.blocked_transformers == other.blocked_transformers
+            and self.column_name_and_types == other.column_name_and_types
+            and self.transformer_params == other.transformer_params
+            and self.mode == other.mode
+            and self.enable_dnn_featurization == other.enable_dnn_featurization
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/forecasting_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/forecasting_job.py
new file mode 100644
index 00000000..9bd10b19
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/forecasting_job.py
@@ -0,0 +1,686 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict, List, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Forecasting as RestForecasting
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ForecastingPrimaryMetrics, JobBase, TaskType
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants import TabularTrainingMode
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.stack_ensemble_settings import StackEnsembleSettings
+from azure.ai.ml.entities._job.automl.tabular.automl_tabular import AutoMLTabular
+from azure.ai.ml.entities._job.automl.tabular.featurization_settings import TabularFeaturizationSettings
+from azure.ai.ml.entities._job.automl.tabular.forecasting_settings import ForecastingSettings
+from azure.ai.ml.entities._job.automl.tabular.limit_settings import TabularLimitSettings
+from azure.ai.ml.entities._job.automl.training_settings import ForecastingTrainingSettings
+from azure.ai.ml.entities._util import load_from_dict
+
+
+class ForecastingJob(AutoMLTabular):
+    """
+    Configuration for AutoML Forecasting Task.
+
+    :param primary_metric: The primary metric to use for model selection.
+    :type primary_metric: Optional[str]
+    :param forecasting_settings: The settings for the forecasting task.
+    :type forecasting_settings:
+        Optional[~azure.ai.ml.automl.ForecastingSettings]
+    :param kwargs: Job-specific arguments
+    :type kwargs: Dict[str, Any]
+    """
+
+    _DEFAULT_PRIMARY_METRIC = ForecastingPrimaryMetrics.NORMALIZED_ROOT_MEAN_SQUARED_ERROR
+
+    def __init__(
+        self,
+        *,
+        primary_metric: Optional[str] = None,
+        forecasting_settings: Optional[ForecastingSettings] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a new AutoML Forecasting task."""
+        # Extract any task specific settings
+        featurization = kwargs.pop("featurization", None)
+        limits = kwargs.pop("limits", None)
+        training = kwargs.pop("training", None)
+
+        super().__init__(
+            task_type=TaskType.FORECASTING,
+            featurization=featurization,
+            limits=limits,
+            training=training,
+            **kwargs,
+        )
+
+        self.primary_metric = primary_metric or ForecastingJob._DEFAULT_PRIMARY_METRIC
+        self._forecasting_settings = forecasting_settings
+
+    @property
+    def primary_metric(self) -> Optional[str]:
+        """
+        Return the primary metric to use for model selection.
+
+        :return: The primary metric for model selection.
+        :rtype: Optional[str]
+        """
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, ForecastingPrimaryMetrics]) -> None:
+        """
+        Set the primary metric to use for model selection.
+
+        :param value: The primary metric for model selection.
+        :type: Union[str, ~azure.ai.ml.automl.ForecastingPrimaryMetrics]
+        """
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+        self._primary_metric = (
+            ForecastingJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else ForecastingPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    @property  # type: ignore
+    def training(self) -> ForecastingTrainingSettings:
+        """
+        Return the forecast training settings.
+
+        :return: training settings.
+        :rtype: ~azure.ai.ml.automl.ForecastingTrainingSettings
+        """
+        return self._training or ForecastingTrainingSettings()
+
+    @training.setter
+    def training(self, value: Union[Dict, ForecastingTrainingSettings]) -> None:  # pylint: disable=unused-argument
+        ...
+
+    @property
+    def forecasting_settings(self) -> Optional[ForecastingSettings]:
+        """
+        Return the forecast settings.
+
+        :return: forecast settings.
+        :rtype: ~azure.ai.ml.automl.ForecastingSettings
+        """
+        return self._forecasting_settings
+
+    def set_forecast_settings(
+        self,
+        *,
+        time_column_name: Optional[str] = None,
+        forecast_horizon: Optional[Union[str, int]] = None,
+        time_series_id_column_names: Optional[Union[str, List[str]]] = None,
+        target_lags: Optional[Union[str, int, List[int]]] = None,
+        feature_lags: Optional[str] = None,
+        target_rolling_window_size: Optional[Union[str, int]] = None,
+        country_or_region_for_holidays: Optional[str] = None,
+        use_stl: Optional[str] = None,
+        seasonality: Optional[Union[str, int]] = None,
+        short_series_handling_config: Optional[str] = None,
+        frequency: Optional[str] = None,
+        target_aggregate_function: Optional[str] = None,
+        cv_step_size: Optional[int] = None,
+        features_unknown_at_forecast_time: Optional[Union[str, List[str]]] = None,
+    ) -> None:
+        """Manage parameters used by forecasting tasks.
+
+        :keyword time_column_name:
+            The name of the time column. This parameter is required when forecasting to specify the datetime
+            column in the input data used for building the time series and inferring its frequency.
+        :paramtype time_column_name: Optional[str]
+        :keyword forecast_horizon:
+            The desired maximum forecast horizon in units of time-series frequency. The default value is 1.
+
+            Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster
+            should predict out. When task type is forecasting, this parameter is required. For more information on
+            setting forecasting parameters, see `Auto-train a time-series forecast model <https://learn.microsoft.com/
+            azure/machine-learning/how-to-auto-train-forecast>`_.
+        :type forecast_horizon: Optional[Union[int, str]]
+        :keyword time_series_id_column_names:
+            The names of columns used to group a time series.
+            It can be used to create multiple series. If time series id column names is not defined or
+            the identifier columns specified do not identify all the series in the dataset, the time series identifiers
+            will be automatically created for your data set.
+        :paramtype time_series_id_column_names: Optional[Union[str, List[str]]]
+        :keyword target_lags: The number of past periods to lag from the target column. By default the lags are turned
+            off.
+
+            When forecasting, this parameter represents the number of rows to lag the target values based
+            on the frequency of the data. This is represented as a list or single integer. Lag should be used
+            when the relationship between the independent variables and dependent variable do not match up or
+            correlate by default. For example, when trying to forecast demand for a product, the demand in any
+            month may depend on the price of specific commodities 3 months prior. In this example, you may want
+            to lag the target (demand) negatively by 3 months so that the model is training on the correct
+            relationship. For more information, see `Auto-train a time-series forecast model
+            <https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-forecast>`_.
+
+            **Note on auto detection of target lags and rolling window size.
+            Please see the corresponding comments in the rolling window section.**
+            We use the next algorithm to detect the optimal target lag and rolling window size.
+
+            #. Estimate the maximum lag order for the look back feature selection. In our case it is the number of
+               periods till the next date frequency granularity i.e. if frequency is daily, it will be a week (7),
+               if it is a week, it will be month (4). That values multiplied by two is the largest
+               possible values of lags/rolling windows. In our examples, we will consider the maximum lag
+               order of 14 and 8 respectively).
+            #. Create a de-seasonalized series by adding trend and residual components. This will be used
+               in the next step.
+            #. Estimate the PACF - Partial Auto Correlation Function on the on the data from (2)
+               and search for points, where the auto correlation is significant i.e. its absolute
+               value is more then 1.96/square_root(maximal lag value), which correspond to significance of 95%.
+            #. If all points are significant, we consider it being strong seasonality
+               and do not create look back features.
+            #. We scan the PACF values from the beginning and the value before the first insignificant
+               auto correlation will designate the lag. If first significant element (value correlate with
+               itself) is followed by insignificant, the lag will be 0 and we will not use look back features.
+
+        :type target_lags: Optional[Union[str, int, List[int]]]
+        :keyword feature_lags: Flag for generating lags for the numeric features with 'auto' or None.
+        :paramtype feature_lags: Optional[str]
+        :keyword target_rolling_window_size: The number of past periods used to create a rolling window average of the
+            target column.
+
+            When forecasting, this parameter represents `n` historical periods to use to generate forecasted values,
+            <= training set size. If omitted, `n` is the full training set size. Specify this parameter
+            when you only want to consider a certain amount of history when training the model.
+            If set to 'auto', rolling window will be estimated as the last
+            value where the PACF is more then the significance threshold. Please see target_lags section for details.
+        :paramtype target_rolling_window_size: Optional[Union[str, int]]
+        :keyword country_or_region_for_holidays: The country/region used to generate holiday features.
+            These should be ISO 3166 two-letter country/region codes, for example 'US' or 'GB'.
+        :paramtype country_or_region_for_holidays: Optional[str]
+        :keyword use_stl: Configure STL Decomposition of the time-series target column.
+            use_stl can take three values: None (default) - no stl decomposition, 'season' - only generate
+            season component and season_trend - generate both season and trend components.
+        :type use_stl: Optional[str]
+        :keyword seasonality: Set time series seasonality as an integer multiple of the series frequency.
+            If seasonality is set to 'auto', it will be inferred.
+            If set to None, the time series is assumed non-seasonal which is equivalent to seasonality=1.
+        :paramtype seasonality: Optional[Union[int, str]
+        :keyword short_series_handling_config:
+            The parameter defining how if AutoML should handle short time series.
+
+            Possible values: 'auto' (default), 'pad', 'drop' and None.
+
+            * **auto** short series will be padded if there are no long series,
+                otherwise short series will be dropped.
+            * **pad** all the short series will be padded.
+            * **drop**  all the short series will be dropped".
+            * **None** the short series will not be modified.
+
+            If set to 'pad', the table will be padded with the zeroes and
+            empty values for the regressors and random values for target with the mean
+            equal to target value median for given time series id. If median is more or equal
+            to zero, the minimal padded value will be clipped by zero:
+            Input:
+
+            +------------+---------------+----------+--------+
+            | Date       | numeric_value | string   | target |
+            +============+===============+==========+========+
+            | 2020-01-01 | 23            | green    | 55     |
+            +------------+---------------+----------+--------+
+
+            Output assuming minimal number of values is four:
+
+            +------------+---------------+----------+--------+
+            | Date       | numeric_value | string   | target |
+            +============+===============+==========+========+
+            | 2019-12-29 | 0             | NA       | 55.1   |
+            +------------+---------------+----------+--------+
+            | 2019-12-30 | 0             | NA       | 55.6   |
+            +------------+---------------+----------+--------+
+            | 2019-12-31 | 0             | NA       | 54.5   |
+            +------------+---------------+----------+--------+
+            | 2020-01-01 | 23            | green    | 55     |
+            +------------+---------------+----------+--------+
+
+            **Note:** We have two parameters short_series_handling_configuration and
+            legacy short_series_handling. When both parameters are set we are
+            synchronize them as shown in the table below (short_series_handling_configuration and
+            short_series_handling for brevity are marked as handling_configuration and handling
+            respectively).
+
+            +------------+--------------------------+----------------------+-----------------------------+
+            | | handling | | handling               | | resulting          | | resulting                 |
+            |            | | configuration          | | handling           | | handling                  |
+            |            |                          |                      | | configuration             |
+            +============+==========================+======================+=============================+
+            | True       | auto                     | True                 | auto                        |
+            +------------+--------------------------+----------------------+-----------------------------+
+            | True       | pad                      | True                 | auto                        |
+            +------------+--------------------------+----------------------+-----------------------------+
+            | True       | drop                     | True                 | auto                        |
+            +------------+--------------------------+----------------------+-----------------------------+
+            | True       | None                     | False                | None                        |
+            +------------+--------------------------+----------------------+-----------------------------+
+            | False      | auto                     | False                | None                        |
+            +------------+--------------------------+----------------------+-----------------------------+
+            | False      | pad                      | False                | None                        |
+            +------------+--------------------------+----------------------+-----------------------------+
+            | False      | drop                     | False                | None                        |
+            +------------+--------------------------+----------------------+-----------------------------+
+            | False      | None                     | False                | None                        |
+            +------------+--------------------------+----------------------+-----------------------------+
+
+        :type short_series_handling_config: Optional[str]
+        :keyword frequency: Forecast frequency.
+
+            When forecasting, this parameter represents the period with which the forecast is desired,
+            for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
+            You can optionally set it to greater (but not lesser) than dataset frequency.
+            We'll aggregate the data and generate the results at forecast frequency. For example,
+            for daily data, you can set the frequency to be daily, weekly or monthly, but not hourly.
+            The frequency needs to be a pandas offset alias.
+            Please refer to pandas documentation for more information:
+            https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects
+        :type frequency: Optional[str]
+        :keyword target_aggregate_function: The function to be used to aggregate the time series target
+            column to conform to a user specified frequency. If the target_aggregation_function is set,
+            but the freq parameter is not set, the error is raised. The possible target aggregation
+            functions are: "sum", "max", "min" and "mean".
+
+                * The target column values are aggregated based on the specified operation.
+                  Typically, sum is appropriate for most scenarios.
+                * Numerical predictor columns in your data are aggregated by sum, mean, minimum value,
+                  and maximum value. As a result, automated ML generates new columns suffixed with the
+                  aggregation function name and applies the selected aggregate operation.
+                * For categorical predictor columns, the data is aggregated by mode,
+                  the most prominent category in the window.
+                * Date predictor columns are aggregated by minimum value, maximum value and mode.
+
+                +----------------+-------------------------------+--------------------------------------+
+                |     | freq     | | target_aggregation_function | | Data regularity                    |
+                |                |                               | | fixing mechanism                   |
+                +================+===============================+======================================+
+                | None (Default) | None (Default)                | | The aggregation                    |
+                |                |                               | | is not applied.                    |
+                |                |                               | | If the valid                       |
+                |                |                               | | frequency can                      |
+                |                |                               | | not be                             |
+                |                |                               | | determined                         |
+                |                |                               | | the error                          |
+                |                |                               | | will be raised.                    |
+                +----------------+-------------------------------+--------------------------------------+
+                | Some Value     | None (Default)                | | The aggregation                    |
+                |                |                               | | is not applied.                    |
+                |                |                               | | If the number                      |
+                |                |                               | | of data points                     |
+                |                |                               | | compliant to                       |
+                |                |                               | | given frequency                    |
+                |                |                               | | grid is                            |
+                |                |                               | | less then 90%                      |
+                |                |                               | | these points                       |
+                |                |                               | | will be                            |
+                |                |                               | | removed,                           |
+                |                |                               | | otherwise                          |
+                |                |                               | | the error will                     |
+                |                |                               | | be raised.                         |
+                +----------------+-------------------------------+--------------------------------------+
+                | None (Default) | Aggregation function          | | The error about                    |
+                |                |                               | | missing                            |
+                |                |                               | | frequency                          |
+                |                |                               | | parameter is                       |
+                |                |                               | | raised.                            |
+                +----------------+-------------------------------+--------------------------------------+
+                | Some Value     | Aggregation function          | | Aggregate to                       |
+                |                |                               | | frequency using                    |
+                |                |                               | | provided                           |
+                |                |                               | | aggregation                        |
+                |                |                               | | function.                          |
+                +----------------+-------------------------------+--------------------------------------+
+
+        :type target_aggregate_function: Optional[str]
+        :keyword cv_step_size: Number of periods between the origin_time of one CV fold and the next fold.
+            For example, if `n_step` = 3 for daily data, the origin time for each fold will be three days apart.
+        :paramtype cv_step_size: Optional[int]
+        :keyword features_unknown_at_forecast_time: The feature columns that are available for training but
+            unknown at the time of forecast/inference. If features_unknown_at_forecast_time is set to an empty
+            list, it is assumed that all the feature columns in the dataset are known at inference time. If this
+            parameter is not set the support for future features is not enabled.
+        :paramtype features_unknown_at_forecast_time: Optional[Union[str, List[str]]]
+        """
+        self._forecasting_settings = self._forecasting_settings or ForecastingSettings()
+
+        self._forecasting_settings.country_or_region_for_holidays = (
+            country_or_region_for_holidays
+            if country_or_region_for_holidays is not None
+            else self._forecasting_settings.country_or_region_for_holidays
+        )
+        self._forecasting_settings.cv_step_size = (
+            cv_step_size if cv_step_size is not None else self._forecasting_settings.cv_step_size
+        )
+        self._forecasting_settings.forecast_horizon = (
+            forecast_horizon if forecast_horizon is not None else self._forecasting_settings.forecast_horizon
+        )
+        self._forecasting_settings.target_lags = (
+            target_lags if target_lags is not None else self._forecasting_settings.target_lags
+        )
+        self._forecasting_settings.target_rolling_window_size = (
+            target_rolling_window_size
+            if target_rolling_window_size is not None
+            else self._forecasting_settings.target_rolling_window_size
+        )
+        self._forecasting_settings.frequency = (
+            frequency if frequency is not None else self._forecasting_settings.frequency
+        )
+        self._forecasting_settings.feature_lags = (
+            feature_lags if feature_lags is not None else self._forecasting_settings.feature_lags
+        )
+        self._forecasting_settings.seasonality = (
+            seasonality if seasonality is not None else self._forecasting_settings.seasonality
+        )
+        self._forecasting_settings.use_stl = use_stl if use_stl is not None else self._forecasting_settings.use_stl
+        self._forecasting_settings.short_series_handling_config = (
+            short_series_handling_config
+            if short_series_handling_config is not None
+            else self._forecasting_settings.short_series_handling_config
+        )
+        self._forecasting_settings.target_aggregate_function = (
+            target_aggregate_function
+            if target_aggregate_function is not None
+            else self._forecasting_settings.target_aggregate_function
+        )
+        self._forecasting_settings.time_column_name = (
+            time_column_name if time_column_name is not None else self._forecasting_settings.time_column_name
+        )
+        self._forecasting_settings.time_series_id_column_names = (
+            time_series_id_column_names
+            if time_series_id_column_names is not None
+            else self._forecasting_settings.time_series_id_column_names
+        )
+        self._forecasting_settings.features_unknown_at_forecast_time = (
+            features_unknown_at_forecast_time
+            if features_unknown_at_forecast_time is not None
+            else self._forecasting_settings.features_unknown_at_forecast_time
+        )
+
+    # override
+    def set_training(
+        self,
+        *,
+        enable_onnx_compatible_models: Optional[bool] = None,
+        enable_dnn_training: Optional[bool] = None,
+        enable_model_explainability: Optional[bool] = None,
+        enable_stack_ensemble: Optional[bool] = None,
+        enable_vote_ensemble: Optional[bool] = None,
+        stack_ensemble_settings: Optional[StackEnsembleSettings] = None,
+        ensemble_model_download_timeout: Optional[int] = None,
+        allowed_training_algorithms: Optional[List[str]] = None,
+        blocked_training_algorithms: Optional[List[str]] = None,
+        training_mode: Optional[Union[str, TabularTrainingMode]] = None,
+    ) -> None:
+        """
+        The method to configure forecast training related settings.
+
+        :keyword enable_onnx_compatible_models:
+            Whether to enable or disable enforcing the ONNX-compatible models.
+            The default is False. For more information about Open Neural Network Exchange (ONNX) and Azure Machine
+            Learning, see this `article <https://learn.microsoft.com/azure/machine-learning/concept-onnx>`__.
+        :type enable_onnx_compatible: Optional[bool]
+        :keyword enable_dnn_training:
+            Whether to include DNN based models during model selection.
+            However, the default is True for DNN NLP tasks, and it's False for all other AutoML tasks.
+        :paramtype enable_dnn_training: Optional[bool]
+        :keyword enable_model_explainability:
+            Whether to enable explaining the best AutoML model at the end of all AutoML training iterations.
+            For more information, see `Interpretability: model explanations in automated machine learning
+            <https://learn.microsoft.com/azure/machine-learning/how-to-machine-learning-interpretability-automl>`__.
+            , defaults to None
+        :type enable_model_explainability: Optional[bool]
+        :keyword enable_stack_ensemble:
+            Whether to enable/disable StackEnsemble iteration.
+            If `enable_onnx_compatible_models` flag is being set, then StackEnsemble iteration will be disabled.
+            Similarly, for Timeseries tasks, StackEnsemble iteration will be disabled by default, to avoid risks of
+            overfitting due to small training set used in fitting the meta learner.
+            For more information about ensembles, see `Ensemble configuration
+            <https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#ensemble>`__
+            , defaults to None
+        :type enable_stack_ensemble: Optional[bool]
+        :keyword enable_vote_ensemble:
+            Whether to enable/disable VotingEnsemble iteration.
+            For more information about ensembles, see `Ensemble configuration
+            <https://learn.microsoft.com/azure/machine-learning/how-to-configure-auto-train#ensemble>`__
+            , defaults to None
+        :type enable_vote_ensemble: Optional[bool]
+        :keyword stack_ensemble_settings:
+            Settings for StackEnsemble iteration, defaults to None
+        :paramtype stack_ensemble_settings: Optional[StackEnsembleSettings]
+        :keyword ensemble_model_download_timeout:
+            During VotingEnsemble and StackEnsemble model generation,
+            multiple fitted models from the previous child runs are downloaded. Configure this parameter with a
+            higher value than 300 secs, if more time is needed, defaults to None
+        :paramtype ensemble_model_download_timeout: Optional[int]
+        :keyword allowed_training_algorithms:
+            A list of model names to search for an experiment. If not specified,
+            then all models supported for the task are used minus any specified in ``blocked_training_algorithms``
+            or deprecated TensorFlow models, defaults to None
+        :paramtype allowed_training_algorithms: Optional[List[str]]
+        :keyword blocked_training_algorithms:
+            A list of algorithms to ignore for an experiment, defaults to None
+        :paramtype blocked_training_algorithms: Optional[List[str]]
+        :keyword training_mode:
+            [Experimental] The training mode to use.
+            The possible values are-
+
+            * distributed- enables distributed training for supported algorithms.
+
+            * non_distributed- disables distributed training.
+
+            * auto- Currently, it is same as non_distributed. In future, this might change.
+
+            Note: This parameter is in public preview and may change in future.
+        :type training_mode: Optional[Union[~azure.ai.ml.constants.TabularTrainingMode, str]]
+        """
+        super().set_training(
+            enable_onnx_compatible_models=enable_onnx_compatible_models,
+            enable_dnn_training=enable_dnn_training,
+            enable_model_explainability=enable_model_explainability,
+            enable_stack_ensemble=enable_stack_ensemble,
+            enable_vote_ensemble=enable_vote_ensemble,
+            stack_ensemble_settings=stack_ensemble_settings,
+            ensemble_model_download_timeout=ensemble_model_download_timeout,
+            allowed_training_algorithms=allowed_training_algorithms,
+            blocked_training_algorithms=blocked_training_algorithms,
+            training_mode=training_mode,
+        )
+
+        # Disable stack ensemble by default, since it is currently not supported for forecasting tasks
+        if enable_stack_ensemble is None:
+            if self._training is not None:
+                self._training.enable_stack_ensemble = False
+
+    def _to_rest_object(self) -> JobBase:
+        if self._forecasting_settings is not None:
+            forecasting_task = RestForecasting(
+                target_column_name=self.target_column_name,
+                training_data=self.training_data,
+                validation_data=self.validation_data,
+                validation_data_size=self.validation_data_size,
+                weight_column_name=self.weight_column_name,
+                cv_split_column_names=self.cv_split_column_names,
+                n_cross_validations=self.n_cross_validations,
+                test_data=self.test_data,
+                test_data_size=self.test_data_size,
+                featurization_settings=self._featurization._to_rest_object() if self._featurization else None,
+                limit_settings=self._limits._to_rest_object() if self._limits else None,
+                training_settings=self._training._to_rest_object() if self._training else None,
+                primary_metric=self.primary_metric,
+                log_verbosity=self.log_verbosity,
+                forecasting_settings=self._forecasting_settings._to_rest_object(),
+            )
+        else:
+            forecasting_task = RestForecasting(
+                target_column_name=self.target_column_name,
+                training_data=self.training_data,
+                validation_data=self.validation_data,
+                validation_data_size=self.validation_data_size,
+                weight_column_name=self.weight_column_name,
+                cv_split_column_names=self.cv_split_column_names,
+                n_cross_validations=self.n_cross_validations,
+                test_data=self.test_data,
+                test_data_size=self.test_data_size,
+                featurization_settings=self._featurization._to_rest_object() if self._featurization else None,
+                limit_settings=self._limits._to_rest_object() if self._limits else None,
+                training_settings=self._training._to_rest_object() if self._training else None,
+                primary_metric=self.primary_metric,
+                log_verbosity=self.log_verbosity,
+                forecasting_settings=None,
+            )
+
+        self._resolve_data_inputs(forecasting_task)
+        self._validation_data_to_rest(forecasting_task)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=forecasting_task,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "ForecastingJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestForecasting = properties.task_details
+
+        job_args_dict = {
+            "id": obj.id,
+            "name": obj.name,
+            "description": properties.description,
+            "tags": properties.tags,
+            "properties": properties.properties,
+            "experiment_name": properties.experiment_name,
+            "services": properties.services,
+            "status": properties.status,
+            "creation_context": obj.system_data,
+            "display_name": properties.display_name,
+            "compute": properties.compute_id,
+            "outputs": from_rest_data_outputs(properties.outputs),
+            "resources": properties.resources,
+            "identity": (
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            "queue_settings": properties.queue_settings,
+        }
+
+        forecasting_job = cls(
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            validation_data_size=task_details.validation_data_size,
+            weight_column_name=task_details.weight_column_name,
+            cv_split_column_names=task_details.cv_split_column_names,
+            n_cross_validations=task_details.n_cross_validations,
+            test_data=task_details.test_data,
+            test_data_size=task_details.test_data_size,
+            featurization=(
+                TabularFeaturizationSettings._from_rest_object(task_details.featurization_settings)
+                if task_details.featurization_settings
+                else None
+            ),
+            limits=(
+                TabularLimitSettings._from_rest_object(task_details.limit_settings)
+                if task_details.limit_settings
+                else None
+            ),
+            training=(
+                ForecastingTrainingSettings._from_rest_object(task_details.training_settings)
+                if task_details.training_settings
+                else None
+            ),
+            primary_metric=task_details.primary_metric,
+            forecasting_settings=(
+                ForecastingSettings._from_rest_object(task_details.forecasting_settings)
+                if task_details.forecasting_settings
+                else None
+            ),
+            log_verbosity=task_details.log_verbosity,
+            **job_args_dict,
+        )
+
+        forecasting_job._restore_data_inputs()
+        forecasting_job._validation_data_from_rest()
+
+        return forecasting_job
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "ForecastingJob":
+        from azure.ai.ml._schema.automl.table_vertical.forecasting import AutoMLForecastingSchema
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLForecastingNodeSchema
+
+        if kwargs.pop("inside_pipeline", False):
+            loaded_data = load_from_dict(AutoMLForecastingNodeSchema, data, context, additional_message, **kwargs)
+        else:
+            loaded_data = load_from_dict(AutoMLForecastingSchema, data, context, additional_message, **kwargs)
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "ForecastingJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        data_settings = {
+            "training_data": loaded_data.pop("training_data"),
+            "target_column_name": loaded_data.pop("target_column_name"),
+            "weight_column_name": loaded_data.pop("weight_column_name", None),
+            "validation_data": loaded_data.pop("validation_data", None),
+            "validation_data_size": loaded_data.pop("validation_data_size", None),
+            "cv_split_column_names": loaded_data.pop("cv_split_column_names", None),
+            "n_cross_validations": loaded_data.pop("n_cross_validations", None),
+            "test_data": loaded_data.pop("test_data", None),
+            "test_data_size": loaded_data.pop("test_data_size", None),
+        }
+        job = ForecastingJob(**loaded_data)
+        job.set_data(**data_settings)
+        return job
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.table_vertical.forecasting import AutoMLForecastingSchema
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLForecastingNodeSchema
+
+        schema_dict: dict = {}
+        if inside_pipeline:
+            schema_dict = AutoMLForecastingNodeSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        else:
+            schema_dict = AutoMLForecastingSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return schema_dict
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ForecastingJob):
+            return NotImplemented
+
+        if not super(ForecastingJob, self).__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric and self._forecasting_settings == other._forecasting_settings
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/forecasting_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/forecasting_settings.py
new file mode 100644
index 00000000..09439483
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/forecasting_settings.py
@@ -0,0 +1,383 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=too-many-instance-attributes
+
+from typing import List, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    AutoForecastHorizon,
+    AutoSeasonality,
+    AutoTargetLags,
+    AutoTargetRollingWindowSize,
+    CustomForecastHorizon,
+    CustomSeasonality,
+    CustomTargetLags,
+    CustomTargetRollingWindowSize,
+    ForecastHorizonMode,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    ForecastingSettings as RestForecastingSettings,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    SeasonalityMode,
+    TargetLagsMode,
+    TargetRollingWindowSizeMode,
+)
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class ForecastingSettings(RestTranslatableMixin):
+    """Forecasting settings for an AutoML Job.
+
+    :param country_or_region_for_holidays: The country/region used to generate holiday features. These should be ISO
+        3166 two-letter country/region code, for example 'US' or 'GB'.
+    :type country_or_region_for_holidays: Optional[str]
+    :param cv_step_size:
+        Number of periods between the origin_time of one CV fold and the next fold. For
+        example, if `n_step` = 3 for daily data, the origin time for each fold will be
+        three days apart.
+    :type cv_step_size: Optional[int]
+    :param forecast_horizon:
+        The desired maximum forecast horizon in units of time-series frequency. The default value is 1.
+
+        Units are based on the time interval of your training data, e.g., monthly, weekly that the forecaster
+        should predict out. When task type is forecasting, this parameter is required. For more information on
+        setting forecasting parameters, see `Auto-train a time-series forecast model <https://learn.microsoft.com/
+        azure/machine-learning/how-to-auto-train-forecast>`_.
+    :type forecast_horizon: Optional[Union[int, str]]
+    :param target_lags:
+        The number of past periods to lag from the target column. By default the lags are turned off.
+
+        When forecasting, this parameter represents the number of rows to lag the target values based
+        on the frequency of the data. This is represented as a list or single integer. Lag should be used
+        when the relationship between the independent variables and dependent variable do not match up or
+        correlate by default. For example, when trying to forecast demand for a product, the demand in any
+        month may depend on the price of specific commodities 3 months prior. In this example, you may want
+        to lag the target (demand) negatively by 3 months so that the model is training on the correct
+        relationship. For more information, see `Auto-train a time-series forecast model
+        <https://learn.microsoft.com/azure/machine-learning/how-to-auto-train-forecast>`_.
+
+        **Note on auto detection of target lags and rolling window size.
+        Please see the corresponding comments in the rolling window section.**
+        We use the next algorithm to detect the optimal target lag and rolling window size.
+
+        #. Estimate the maximum lag order for the look back feature selection. In our case it is the number of
+           periods till the next date frequency granularity i.e. if frequency is daily, it will be a week (7),
+           if it is a week, it will be month (4). That values multiplied by two is the largest
+           possible values of lags/rolling windows. In our examples, we will consider the maximum lag
+           order of 14 and 8 respectively).
+        #. Create a de-seasonalized series by adding trend and residual components. This will be used
+           in the next step.
+        #. Estimate the PACF - Partial Auto Correlation Function on the on the data from (2)
+           and search for points, where the auto correlation is significant i.e. its absolute
+           value is more then 1.96/square_root(maximal lag value), which correspond to significance of 95%.
+        #. If all points are significant, we consider it being strong seasonality
+           and do not create look back features.
+        #. We scan the PACF values from the beginning and the value before the first insignificant
+           auto correlation will designate the lag. If first significant element (value correlate with
+           itself) is followed by insignificant, the lag will be 0 and we will not use look back features.
+    :type target_lags: Union[str, int, List[int]]
+    :param target_rolling_window_size:
+        The number of past periods used to create a rolling window average of the target column.
+
+        When forecasting, this parameter represents `n` historical periods to use to generate forecasted values,
+        <= training set size. If omitted, `n` is the full training set size. Specify this parameter
+        when you only want to consider a certain amount of history when training the model.
+        If set to 'auto', rolling window will be estimated as the last
+        value where the PACF is more then the significance threshold. Please see target_lags section for details.
+    :type target_rolling_window_size: Optional[Union[str, int]]
+    :param frequency: Forecast frequency.
+
+        When forecasting, this parameter represents the period with which the forecast is desired,
+        for example daily, weekly, yearly, etc. The forecast frequency is dataset frequency by default.
+        You can optionally set it to greater (but not lesser) than dataset frequency.
+        We'll aggregate the data and generate the results at forecast frequency. For example,
+        for daily data, you can set the frequency to be daily, weekly or monthly, but not hourly.
+        The frequency needs to be a pandas offset alias.
+        Please refer to pandas documentation for more information:
+        https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects
+    :type frequency: Optional[str]
+    :param feature_lags: Flag for generating lags for the numeric features with 'auto' or None.
+    :type feature_lags: Optional[str]
+    :param seasonality: Set time series seasonality as an integer multiple of the series frequency.
+                If seasonality is set to 'auto', it will be inferred.
+                If set to None, the time series is assumed non-seasonal which is equivalent to seasonality=1.
+    :type seasonality: Optional[Union[int, str]]
+    :param use_stl: Configure STL Decomposition of the time-series target column.
+                use_stl can take three values: None (default) - no stl decomposition, 'season' - only generate
+                season component and season_trend - generate both season and trend components.
+    :type use_stl: Optional[str]
+    :param short_series_handling_config:
+        The parameter defining how if AutoML should handle short time series.
+
+        Possible values: 'auto' (default), 'pad', 'drop' and None.
+        * **auto** short series will be padded if there are no long series,
+        otherwise short series will be dropped.
+        * **pad** all the short series will be padded.
+        * **drop**  all the short series will be dropped".
+        * **None** the short series will not be modified.
+        If set to 'pad', the table will be padded with the zeroes and
+        empty values for the regressors and random values for target with the mean
+        equal to target value median for given time series id. If median is more or equal
+        to zero, the minimal padded value will be clipped by zero.
+        Input:
+
+        +------------+---------------+----------+--------+
+        | Date       | numeric_value | string   | target |
+        +============+===============+==========+========+
+        | 2020-01-01 | 23            | green    | 55     |
+        +------------+---------------+----------+--------+
+
+        Output assuming minimal number of values is four:
+
+        +------------+---------------+----------+--------+
+        | Date       | numeric_value | string   | target |
+        +============+===============+==========+========+
+        | 2019-12-29 | 0             | NA       | 55.1   |
+        +------------+---------------+----------+--------+
+        | 2019-12-30 | 0             | NA       | 55.6   |
+        +------------+---------------+----------+--------+
+        | 2019-12-31 | 0             | NA       | 54.5   |
+        +------------+---------------+----------+--------+
+        | 2020-01-01 | 23            | green    | 55     |
+        +------------+---------------+----------+--------+
+
+        **Note:** We have two parameters short_series_handling_configuration and
+        legacy short_series_handling. When both parameters are set we are
+        synchronize them as shown in the table below (short_series_handling_configuration and
+        short_series_handling for brevity are marked as handling_configuration and handling
+        respectively).
+
+        +------------+--------------------------+----------------------+-----------------------------+
+        | | handling | | handling configuration | | resulting handling | | resulting handling        |
+        |            |                          |                      | | configuration             |
+        +============+==========================+======================+=============================+
+        | True       | auto                     | True                 | auto                        |
+        +------------+--------------------------+----------------------+-----------------------------+
+        | True       | pad                      | True                 | auto                        |
+        +------------+--------------------------+----------------------+-----------------------------+
+        | True       | drop                     | True                 | auto                        |
+        +------------+--------------------------+----------------------+-----------------------------+
+        | True       | None                     | False                | None                        |
+        +------------+--------------------------+----------------------+-----------------------------+
+        | False      | auto                     | False                | None                        |
+        +------------+--------------------------+----------------------+-----------------------------+
+        | False      | pad                      | False                | None                        |
+        +------------+--------------------------+----------------------+-----------------------------+
+        | False      | drop                     | False                | None                        |
+        +------------+--------------------------+----------------------+-----------------------------+
+        | False      | None                     | False                | None                        |
+        +------------+--------------------------+----------------------+-----------------------------+
+
+    :type short_series_handling_config: Optional[str]
+    :param target_aggregate_function: The function to be used to aggregate the time series target
+                                      column to conform to a user specified frequency. If the
+                                      target_aggregation_function is set, but the freq parameter
+                                      is not set, the error is raised. The possible target
+                                      aggregation functions are: "sum", "max", "min" and "mean".
+
+            * The target column values are aggregated based on the specified operation.
+              Typically, sum is appropriate for most scenarios.
+            * Numerical predictor columns in your data are aggregated by sum, mean, minimum value,
+              and maximum value. As a result, automated ML generates new columns suffixed with the
+              aggregation function name and applies the selected aggregate operation.
+            * For categorical predictor columns, the data is aggregated by mode,
+              the most prominent category in the window.
+            * Date predictor columns are aggregated by minimum value, maximum value and mode.
+
+            +----------------+-------------------------------+--------------------------------------+
+            |     | freq     | | target_aggregation_function | | Data regularity                    |
+            |                |                               | | fixing mechanism                   |
+            +================+===============================+======================================+
+            | None (Default) | None (Default)                | | The aggregation is not             |
+            |                |                               | | applied. If the valid              |
+            |                |                               | | frequency can not be               |
+            |                |                               | | determined the error will          |
+            |                |                               | | be raised.                         |
+            +----------------+-------------------------------+--------------------------------------+
+            | Some Value     | None (Default)                | | The aggregation is not             |
+            |                |                               | | applied. If the number             |
+            |                |                               | | of data points compliant           |
+            |                |                               | | to given frequency grid            |
+            |                |                               | | is less then 90% these points      |
+            |                |                               | | will be removed, otherwise         |
+            |                |                               | | the error will be raised.          |
+            +----------------+-------------------------------+--------------------------------------+
+            | None (Default) | Aggregation function          | | The error about missing            |
+            |                |                               | | frequency parameter                |
+            |                |                               | | is raised.                         |
+            +----------------+-------------------------------+--------------------------------------+
+            | Some Value     | Aggregation function          | | Aggregate to frequency using       |
+            |                |                               | | provided aggregation function.     |
+            +----------------+-------------------------------+--------------------------------------+
+    :type target_aggregate_function: str
+    :param time_column_name:
+        The name of the time column. This parameter is required when forecasting to specify the datetime
+        column in the input data used for building the time series and inferring its frequency.
+    :type time_column_name: Optional[str]
+    :param time_series_id_column_names:
+        The names of columns used to group a timeseries.
+        It can be used to create multiple series. If time series id column names is not defined or
+        the identifier columns specified do not identify all the series in the dataset, the time series identifiers
+        will be automatically created for your dataset.
+    :type time_series_id_column_names: Union[str, List[str]]
+    :param features_unknown_at_forecast_time:
+        The feature columns that are available for training but unknown at the time of forecast/inference.
+        If features_unknown_at_forecast_time is set to an empty list, it is assumed that
+        all the feature columns in the dataset are known at inference time. If this parameter is not set
+        the support for future features is not enabled.
+    :type features_unknown_at_forecast_time: Optional[Union[str, List[str]]]
+    """
+
+    def __init__(
+        self,
+        *,
+        country_or_region_for_holidays: Optional[str] = None,
+        cv_step_size: Optional[int] = None,
+        forecast_horizon: Optional[Union[str, int]] = None,
+        target_lags: Optional[Union[str, int, List[int]]] = None,
+        target_rolling_window_size: Optional[Union[str, int]] = None,
+        frequency: Optional[str] = None,
+        feature_lags: Optional[str] = None,
+        seasonality: Optional[Union[str, int]] = None,
+        use_stl: Optional[str] = None,
+        short_series_handling_config: Optional[str] = None,
+        target_aggregate_function: Optional[str] = None,
+        time_column_name: Optional[str] = None,
+        time_series_id_column_names: Optional[Union[str, List[str]]] = None,
+        features_unknown_at_forecast_time: Optional[Union[str, List[str]]] = None,
+    ):
+        self.country_or_region_for_holidays = country_or_region_for_holidays
+        self.cv_step_size = cv_step_size
+        self.forecast_horizon = forecast_horizon
+        self.target_lags = target_lags
+        self.target_rolling_window_size = target_rolling_window_size
+        self.frequency = frequency
+        self.feature_lags = feature_lags
+        self.seasonality = seasonality
+        self.use_stl = use_stl
+        self.short_series_handling_config = short_series_handling_config
+        self.target_aggregate_function = target_aggregate_function
+        self.time_column_name = time_column_name
+        self.time_series_id_column_names = time_series_id_column_names
+        self.features_unknown_at_forecast_time = features_unknown_at_forecast_time
+
+    def _to_rest_object(self) -> RestForecastingSettings:
+        forecast_horizon = None
+        if isinstance(self.forecast_horizon, str):
+            forecast_horizon = AutoForecastHorizon()
+        elif self.forecast_horizon:
+            forecast_horizon = CustomForecastHorizon(value=self.forecast_horizon)
+
+        target_lags = None
+        if isinstance(self.target_lags, str):
+            target_lags = AutoTargetLags()
+        elif self.target_lags:
+            lags = [self.target_lags] if not isinstance(self.target_lags, list) else self.target_lags
+            target_lags = CustomTargetLags(values=lags)
+
+        target_rolling_window_size = None
+        if isinstance(self.target_rolling_window_size, str):
+            target_rolling_window_size = AutoTargetRollingWindowSize()
+        elif self.target_rolling_window_size:
+            target_rolling_window_size = CustomTargetRollingWindowSize(value=self.target_rolling_window_size)
+
+        seasonality = None
+        if isinstance(self.seasonality, str):
+            seasonality = AutoSeasonality()
+        elif self.seasonality:
+            seasonality = CustomSeasonality(value=self.seasonality)
+
+        time_series_id_column_names = self.time_series_id_column_names
+        if isinstance(self.time_series_id_column_names, str) and self.time_series_id_column_names:
+            time_series_id_column_names = [self.time_series_id_column_names]
+
+        features_unknown_at_forecast_time = self.features_unknown_at_forecast_time
+        if isinstance(self.features_unknown_at_forecast_time, str) and self.features_unknown_at_forecast_time:
+            features_unknown_at_forecast_time = [self.features_unknown_at_forecast_time]
+
+        return RestForecastingSettings(
+            country_or_region_for_holidays=self.country_or_region_for_holidays,
+            cv_step_size=self.cv_step_size,
+            forecast_horizon=forecast_horizon,
+            time_column_name=self.time_column_name,
+            target_lags=target_lags,
+            target_rolling_window_size=target_rolling_window_size,
+            seasonality=seasonality,
+            frequency=self.frequency,
+            feature_lags=self.feature_lags,
+            use_stl=self.use_stl,
+            short_series_handling_config=self.short_series_handling_config,
+            target_aggregate_function=self.target_aggregate_function,
+            time_series_id_column_names=time_series_id_column_names,
+            features_unknown_at_forecast_time=features_unknown_at_forecast_time,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestForecastingSettings) -> "ForecastingSettings":
+        forecast_horizon = None
+        if obj.forecast_horizon and obj.forecast_horizon.mode == ForecastHorizonMode.AUTO:
+            forecast_horizon = obj.forecast_horizon.mode.lower()
+        elif obj.forecast_horizon:
+            forecast_horizon = obj.forecast_horizon.value
+
+        rest_target_lags = obj.target_lags
+        target_lags = None
+        if rest_target_lags and rest_target_lags.mode == TargetLagsMode.AUTO:
+            target_lags = rest_target_lags.mode.lower()
+        elif rest_target_lags:
+            target_lags = rest_target_lags.values
+
+        target_rolling_window_size = None
+        if obj.target_rolling_window_size and obj.target_rolling_window_size.mode == TargetRollingWindowSizeMode.AUTO:
+            target_rolling_window_size = obj.target_rolling_window_size.mode.lower()
+        elif obj.target_rolling_window_size:
+            target_rolling_window_size = obj.target_rolling_window_size.value
+
+        seasonality = None
+        if obj.seasonality and obj.seasonality.mode == SeasonalityMode.AUTO:
+            seasonality = obj.seasonality.mode.lower()
+        elif obj.seasonality:
+            seasonality = obj.seasonality.value
+
+        return cls(
+            country_or_region_for_holidays=obj.country_or_region_for_holidays,
+            cv_step_size=obj.cv_step_size,
+            forecast_horizon=forecast_horizon,
+            target_lags=target_lags,
+            target_rolling_window_size=target_rolling_window_size,
+            frequency=obj.frequency,
+            feature_lags=obj.feature_lags,
+            seasonality=seasonality,
+            use_stl=obj.use_stl,
+            short_series_handling_config=obj.short_series_handling_config,
+            target_aggregate_function=obj.target_aggregate_function,
+            time_column_name=obj.time_column_name,
+            time_series_id_column_names=obj.time_series_id_column_names,
+            features_unknown_at_forecast_time=obj.features_unknown_at_forecast_time,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ForecastingSettings):
+            return NotImplemented
+        return (
+            self.country_or_region_for_holidays == other.country_or_region_for_holidays
+            and self.cv_step_size == other.cv_step_size
+            and self.forecast_horizon == other.forecast_horizon
+            and self.target_lags == other.target_lags
+            and self.target_rolling_window_size == other.target_rolling_window_size
+            and self.frequency == other.frequency
+            and self.feature_lags == other.feature_lags
+            and self.seasonality == other.seasonality
+            and self.use_stl == other.use_stl
+            and self.short_series_handling_config == other.short_series_handling_config
+            and self.target_aggregate_function == other.target_aggregate_function
+            and self.time_column_name == other.time_column_name
+            and self.time_series_id_column_names == other.time_series_id_column_names
+            and self.features_unknown_at_forecast_time == other.features_unknown_at_forecast_time
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/limit_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/limit_settings.py
new file mode 100644
index 00000000..1024f504
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/limit_settings.py
@@ -0,0 +1,101 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import TableVerticalLimitSettings as RestTabularLimitSettings
+from azure.ai.ml._utils.utils import from_iso_duration_format_mins, to_iso_duration_format_mins
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class TabularLimitSettings(RestTranslatableMixin):
+    """Limit settings for a AutoML Table Verticals.
+
+    :param enable_early_termination: Whether to enable early termination if the score is not improving in
+        the short term. The default is True.
+    :type enable_early_termination: bool
+    :param exit_score: Target score for experiment. The experiment terminates after this score is reached.
+    :type exit_score: float
+    :param max_concurrent_trials: Maximum number of concurrent AutoML iterations.
+    :type max_concurrent_trials: int
+    :param max_cores_per_trial: The maximum number of threads to use for a given training iteration.
+    :type max_cores_per_trial: int
+    :param max_nodes: [Experimental] The maximum number of nodes to use for distributed training.
+
+        * For forecasting, each model is trained using max(2, int(max_nodes / max_concurrent_trials)) nodes.
+
+        * For classification/regression, each model is trained using max_nodes nodes.
+
+        Note- This parameter is in public preview and might change in future.
+    :type max_nodes: int
+    :param max_trials: Maximum number of AutoML iterations.
+    :type max_trials: int
+    :param timeout_minutes: AutoML job timeout.
+    :type timeout_minutes: int
+    :param trial_timeout_minutes: AutoML job timeout.
+    :type trial_timeout_minutes: int
+    """
+
+    def __init__(
+        self,
+        *,
+        enable_early_termination: Optional[bool] = None,
+        exit_score: Optional[float] = None,
+        max_concurrent_trials: Optional[int] = None,
+        max_cores_per_trial: Optional[int] = None,
+        max_nodes: Optional[int] = None,
+        max_trials: Optional[int] = None,
+        timeout_minutes: Optional[int] = None,
+        trial_timeout_minutes: Optional[int] = None,
+    ):
+        self.enable_early_termination = enable_early_termination
+        self.exit_score = exit_score
+        self.max_concurrent_trials = max_concurrent_trials
+        self.max_cores_per_trial = max_cores_per_trial
+        self.max_nodes = max_nodes
+        self.max_trials = max_trials
+        self.timeout_minutes = timeout_minutes
+        self.trial_timeout_minutes = trial_timeout_minutes
+
+    def _to_rest_object(self) -> RestTabularLimitSettings:
+        return RestTabularLimitSettings(
+            enable_early_termination=self.enable_early_termination,
+            exit_score=self.exit_score,
+            max_concurrent_trials=self.max_concurrent_trials,
+            max_cores_per_trial=self.max_cores_per_trial,
+            max_nodes=self.max_nodes,
+            max_trials=self.max_trials,
+            timeout=to_iso_duration_format_mins(self.timeout_minutes),
+            trial_timeout=to_iso_duration_format_mins(self.trial_timeout_minutes),
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestTabularLimitSettings) -> "TabularLimitSettings":
+        return cls(
+            enable_early_termination=obj.enable_early_termination,
+            exit_score=obj.exit_score,
+            max_concurrent_trials=obj.max_concurrent_trials,
+            max_cores_per_trial=obj.max_cores_per_trial,
+            max_nodes=obj.max_nodes,
+            max_trials=obj.max_trials,
+            timeout_minutes=from_iso_duration_format_mins(obj.timeout),
+            trial_timeout_minutes=from_iso_duration_format_mins(obj.trial_timeout),
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, TabularLimitSettings):
+            return NotImplemented
+        return (
+            self.enable_early_termination == other.enable_early_termination
+            and self.exit_score == other.exit_score
+            and self.max_concurrent_trials == other.max_concurrent_trials
+            and self.max_cores_per_trial == other.max_cores_per_trial
+            and self.max_nodes == other.max_nodes
+            and self.max_trials == other.max_trials
+            and self.timeout_minutes == other.timeout_minutes
+            and self.trial_timeout_minutes == other.trial_timeout_minutes
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/regression_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/regression_job.py
new file mode 100644
index 00000000..3531e52c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/tabular/regression_job.py
@@ -0,0 +1,239 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AutoMLJob as RestAutoMLJob
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Regression as RestRegression
+from azure.ai.ml._restclient.v2023_04_01_preview.models import RegressionPrimaryMetrics, TaskType
+from azure.ai.ml._utils.utils import camel_to_snake, is_data_binding_expression
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._job.automl import AutoMLConstants
+from azure.ai.ml.entities._credentials import _BaseJobIdentityConfiguration
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.automl.tabular import AutoMLTabular, TabularFeaturizationSettings, TabularLimitSettings
+from azure.ai.ml.entities._job.automl.training_settings import RegressionTrainingSettings
+from azure.ai.ml.entities._util import load_from_dict
+
+
+class RegressionJob(AutoMLTabular):
+    """Configuration for AutoML Regression Job."""
+
+    _DEFAULT_PRIMARY_METRIC = RegressionPrimaryMetrics.NORMALIZED_ROOT_MEAN_SQUARED_ERROR
+
+    def __init__(
+        self,
+        *,
+        primary_metric: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        """Initialize a new AutoML Regression task.
+
+        :param primary_metric: The primary metric to use for optimization
+        :type primary_metric: str
+        :param kwargs: Job-specific arguments
+        :type kwargs: dict
+        """
+        # Extract any task specific settings
+        featurization = kwargs.pop("featurization", None)
+        limits = kwargs.pop("limits", None)
+        training = kwargs.pop("training", None)
+
+        super().__init__(
+            task_type=TaskType.REGRESSION,
+            featurization=featurization,
+            limits=limits,
+            training=training,
+            **kwargs,
+        )
+
+        self.primary_metric = primary_metric or RegressionJob._DEFAULT_PRIMARY_METRIC
+
+    @property
+    def primary_metric(self) -> Union[str, RegressionPrimaryMetrics]:
+        return self._primary_metric
+
+    @primary_metric.setter
+    def primary_metric(self, value: Union[str, RegressionPrimaryMetrics]) -> None:
+        # TODO: better way to do this
+        if is_data_binding_expression(str(value), ["parent"]):
+            self._primary_metric = value
+            return
+        self._primary_metric = (
+            RegressionJob._DEFAULT_PRIMARY_METRIC
+            if value is None
+            else RegressionPrimaryMetrics[camel_to_snake(value).upper()]
+        )
+
+    @property
+    def training(self) -> RegressionTrainingSettings:
+        return self._training or RegressionTrainingSettings()
+
+    @training.setter
+    def training(self, value: Union[Dict, RegressionTrainingSettings]) -> None:  # pylint: disable=unused-argument
+        ...
+
+    def _to_rest_object(self) -> JobBase:
+        regression_task = RestRegression(
+            target_column_name=self.target_column_name,
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            validation_data_size=self.validation_data_size,
+            weight_column_name=self.weight_column_name,
+            cv_split_column_names=self.cv_split_column_names,
+            n_cross_validations=self.n_cross_validations,
+            test_data=self.test_data,
+            test_data_size=self.test_data_size,
+            featurization_settings=self._featurization._to_rest_object() if self._featurization else None,
+            limit_settings=self._limits._to_rest_object() if self._limits else None,
+            training_settings=self._training._to_rest_object() if self._training else None,
+            primary_metric=self.primary_metric,
+            log_verbosity=self.log_verbosity,
+        )
+        self._resolve_data_inputs(regression_task)
+        self._validation_data_to_rest(regression_task)
+
+        properties = RestAutoMLJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            compute_id=self.compute,
+            properties=self.properties,
+            environment_id=self.environment_id,
+            environment_variables=self.environment_variables,
+            services=self.services,
+            outputs=to_rest_data_outputs(self.outputs),
+            resources=self.resources,
+            task_details=regression_task,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings,
+        )
+
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _from_rest_object(cls, obj: JobBase) -> "RegressionJob":
+        properties: RestAutoMLJob = obj.properties
+        task_details: RestRegression = properties.task_details
+
+        job_args_dict = {
+            "id": obj.id,
+            "name": obj.name,
+            "description": properties.description,
+            "tags": properties.tags,
+            "properties": properties.properties,
+            "experiment_name": properties.experiment_name,
+            "services": properties.services,
+            "status": properties.status,
+            "creation_context": obj.system_data,
+            "display_name": properties.display_name,
+            "compute": properties.compute_id,
+            "outputs": from_rest_data_outputs(properties.outputs),
+            "resources": properties.resources,
+            "identity": (
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            "queue_settings": properties.queue_settings,
+        }
+
+        regression_job = cls(
+            target_column_name=task_details.target_column_name,
+            training_data=task_details.training_data,
+            validation_data=task_details.validation_data,
+            validation_data_size=task_details.validation_data_size,
+            weight_column_name=task_details.weight_column_name,
+            cv_split_column_names=task_details.cv_split_column_names,
+            n_cross_validations=task_details.n_cross_validations,
+            test_data=task_details.test_data,
+            test_data_size=task_details.test_data_size,
+            featurization=(
+                TabularFeaturizationSettings._from_rest_object(task_details.featurization_settings)
+                if task_details.featurization_settings
+                else None
+            ),
+            limits=(
+                TabularLimitSettings._from_rest_object(task_details.limit_settings)
+                if task_details.limit_settings
+                else None
+            ),
+            training=(
+                RegressionTrainingSettings._from_rest_object(task_details.training_settings)
+                if task_details.training_settings
+                else None
+            ),
+            primary_metric=task_details.primary_metric,
+            log_verbosity=task_details.log_verbosity,
+            **job_args_dict,
+        )
+
+        regression_job._restore_data_inputs()
+        regression_job._validation_data_from_rest()
+
+        return regression_job
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "RegressionJob":
+        from azure.ai.ml._schema.automl.table_vertical.regression import AutoMLRegressionSchema
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLRegressionNodeSchema
+
+        if kwargs.pop("inside_pipeline", False):
+            loaded_data = load_from_dict(AutoMLRegressionNodeSchema, data, context, additional_message, **kwargs)
+        else:
+            loaded_data = load_from_dict(AutoMLRegressionSchema, data, context, additional_message, **kwargs)
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "RegressionJob":
+        loaded_data.pop(AutoMLConstants.TASK_TYPE_YAML, None)
+        data_settings = {
+            "training_data": loaded_data.pop("training_data"),
+            "target_column_name": loaded_data.pop("target_column_name"),
+            "weight_column_name": loaded_data.pop("weight_column_name", None),
+            "validation_data": loaded_data.pop("validation_data", None),
+            "validation_data_size": loaded_data.pop("validation_data_size", None),
+            "cv_split_column_names": loaded_data.pop("cv_split_column_names", None),
+            "n_cross_validations": loaded_data.pop("n_cross_validations", None),
+            "test_data": loaded_data.pop("test_data", None),
+            "test_data_size": loaded_data.pop("test_data_size", None),
+        }
+        job = RegressionJob(**loaded_data)
+        job.set_data(**data_settings)
+        return job
+
+    def _to_dict(self, inside_pipeline: bool = False) -> Dict:
+        from azure.ai.ml._schema.automl.table_vertical.regression import AutoMLRegressionSchema
+        from azure.ai.ml._schema.pipeline.automl_node import AutoMLRegressionNodeSchema
+
+        schema_dict: dict = {}
+        if inside_pipeline:
+            schema_dict = AutoMLRegressionNodeSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        else:
+            schema_dict = AutoMLRegressionSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+
+        return schema_dict
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, RegressionJob):
+            return NotImplemented
+
+        if not super(RegressionJob, self).__eq__(other):
+            return False
+
+        return self.primary_metric == other.primary_metric
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/training_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/training_settings.py
new file mode 100644
index 00000000..97bc7e17
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/training_settings.py
@@ -0,0 +1,357 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=R0902,protected-access
+
+from typing import Any, List, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ClassificationModels
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    ClassificationTrainingSettings as RestClassificationTrainingSettings,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ForecastingModels
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    ForecastingTrainingSettings as RestForecastingTrainingSettings,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import RegressionModels
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    RegressionTrainingSettings as RestRegressionTrainingSettings,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import TrainingSettings as RestTrainingSettings
+from azure.ai.ml._utils.utils import camel_to_snake, from_iso_duration_format_mins, to_iso_duration_format_mins
+from azure.ai.ml.constants import TabularTrainingMode
+from azure.ai.ml.entities._job.automl.stack_ensemble_settings import StackEnsembleSettings
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+class TrainingSettings(RestTranslatableMixin):
+    """TrainingSettings class for Azure Machine Learning."""
+
+    def __init__(
+        self,
+        *,
+        enable_onnx_compatible_models: Optional[bool] = None,
+        enable_dnn_training: Optional[bool] = None,
+        enable_model_explainability: Optional[bool] = None,
+        enable_stack_ensemble: Optional[bool] = None,
+        enable_vote_ensemble: Optional[bool] = None,
+        stack_ensemble_settings: Optional[StackEnsembleSettings] = None,
+        ensemble_model_download_timeout: Optional[int] = None,
+        allowed_training_algorithms: Optional[List[str]] = None,
+        blocked_training_algorithms: Optional[List[str]] = None,
+        training_mode: Optional[Union[str, TabularTrainingMode]] = None,
+    ):
+        """TrainingSettings class for Azure Machine Learning.
+
+        :param enable_onnx_compatible_models: If set to True, the model will be trained to be compatible with ONNX
+        :type enable_onnx_compatible_models: typing.Optional[bool]
+        :param enable_dnn_training: If set to True,the model will use DNN training
+        :type enable_dnn_training: typing.Optional[bool]
+        :param enable_model_explainability: If set to True, the model will be trained to be explainable
+        :type enable_model_explainability: typing.Optional[bool]
+        :param enable_stack_ensemble: If set to True, a final ensemble model will be created using a stack of models
+        :type enable_stack_ensemble: typing.Optional[bool]
+        :param enable_vote_ensemble: If set to True, a final ensemble model will be created using a voting ensemble
+        :type enable_vote_ensemble: typing.Optional[bool]
+        :param stack_ensemble_settings: Settings for stack ensemble
+        :type stack_ensemble_settings: typing.Optional[azure.ai.ml.automl.StackEnsembleSettings]
+        :param ensemble_model_download_timeout: Timeout for downloading ensemble models
+        :type ensemble_model_download_timeout: typing.Optional[typing.List[int]]
+        :param allowed_training_algorithms: Models to train
+        :type allowed_training_algorithms: typing.Optional[typing.List[str]]
+        :param blocked_training_algorithms: Models that will not be considered for training
+        :type blocked_training_algorithms: typing.Optional[typing.List[str]]
+        :param training_mode: [Experimental] The training mode to use.
+            The possible values are-
+
+            * distributed- enables distributed training for supported algorithms.
+
+            * non_distributed- disables distributed training.
+
+            * auto- Currently, it is same as non_distributed. In future, this might change.
+
+            Note: This parameter is in public preview and may change in future.
+        :type training_mode: typing.Optional[typing.Union[str, azure.ai.ml.constants.TabularTrainingMode]]
+        """
+        self.enable_onnx_compatible_models = enable_onnx_compatible_models
+        self.enable_dnn_training = enable_dnn_training
+        self.enable_model_explainability = enable_model_explainability
+        self.enable_stack_ensemble = enable_stack_ensemble
+        self.enable_vote_ensemble = enable_vote_ensemble
+        self.stack_ensemble_settings = stack_ensemble_settings
+        self.ensemble_model_download_timeout = ensemble_model_download_timeout
+        self.allowed_training_algorithms = allowed_training_algorithms
+        self.blocked_training_algorithms = blocked_training_algorithms
+        self.training_mode = training_mode
+
+    @property
+    def training_mode(self) -> Optional[TabularTrainingMode]:
+        return self._training_mode
+
+    @training_mode.setter
+    def training_mode(self, value: Optional[Union[str, TabularTrainingMode]]) -> None:
+        if value is None or value is TabularTrainingMode:
+            self._training_mode = value
+        elif hasattr(TabularTrainingMode, camel_to_snake(value).upper()):
+            self._training_mode = TabularTrainingMode[camel_to_snake(value).upper()]
+        else:
+            supported_values = ", ".join([f'"{camel_to_snake(mode.value)}"' for mode in TabularTrainingMode])
+            msg = (
+                f"Unsupported training mode: {value}. Supported values are- {supported_values}. "
+                "Or you can use azure.ai.ml.constants.TabularTrainingMode enum."
+            )
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.AUTOML,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    @property
+    def allowed_training_algorithms(self) -> Optional[List[str]]:
+        return self._allowed_training_algorithms
+
+    @allowed_training_algorithms.setter
+    def allowed_training_algorithms(self, value: Optional[List[str]]) -> None:
+        self._allowed_training_algorithms = value
+
+    @property
+    def blocked_training_algorithms(self) -> Optional[List[str]]:
+        return self._blocked_training_algorithms
+
+    @blocked_training_algorithms.setter
+    def blocked_training_algorithms(self, value: Optional[List[str]]) -> None:
+        self._blocked_training_algorithms = value
+
+    def _to_rest_object(self) -> RestTrainingSettings:
+        return RestTrainingSettings(
+            enable_dnn_training=self.enable_dnn_training,
+            enable_onnx_compatible_models=self.enable_onnx_compatible_models,
+            enable_model_explainability=self.enable_model_explainability,
+            enable_stack_ensemble=self.enable_stack_ensemble,
+            enable_vote_ensemble=self.enable_vote_ensemble,
+            stack_ensemble_settings=(
+                self.stack_ensemble_settings._to_rest_object() if self.stack_ensemble_settings else None
+            ),
+            ensemble_model_download_timeout=to_iso_duration_format_mins(self.ensemble_model_download_timeout),
+            training_mode=self.training_mode,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestTrainingSettings) -> "TrainingSettings":
+        return cls(
+            enable_dnn_training=obj.enable_dnn_training,
+            enable_onnx_compatible_models=obj.enable_onnx_compatible_models,
+            enable_model_explainability=obj.enable_model_explainability,
+            enable_stack_ensemble=obj.enable_stack_ensemble,
+            enable_vote_ensemble=obj.enable_vote_ensemble,
+            ensemble_model_download_timeout=from_iso_duration_format_mins(obj.ensemble_model_download_timeout),
+            stack_ensemble_settings=(
+                StackEnsembleSettings._from_rest_object(obj.stack_ensemble_settings)
+                if obj.stack_ensemble_settings
+                else None
+            ),
+            training_mode=obj.training_mode,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, TrainingSettings):
+            return NotImplemented
+        return (
+            self.enable_dnn_training == other.enable_dnn_training
+            and self.enable_onnx_compatible_models == other.enable_onnx_compatible_models
+            and self.enable_model_explainability == other.enable_model_explainability
+            and self.enable_stack_ensemble == other.enable_stack_ensemble
+            and self.enable_vote_ensemble == other.enable_vote_ensemble
+            and self.ensemble_model_download_timeout == other.ensemble_model_download_timeout
+            and self.stack_ensemble_settings == other.stack_ensemble_settings
+            and self.allowed_training_algorithms == other.allowed_training_algorithms
+            and self.blocked_training_algorithms == other.blocked_training_algorithms
+            and self.training_mode == other.training_mode
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
+
+
+class ClassificationTrainingSettings(TrainingSettings):
+    """Classification TrainingSettings class for Azure Machine Learning."""
+
+    def __init__(
+        self,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+
+    @property
+    def allowed_training_algorithms(self) -> Optional[List]:
+        return self._allowed_training_algorithms
+
+    @allowed_training_algorithms.setter
+    def allowed_training_algorithms(self, allowed_model_list: Union[List[str], List[ClassificationModels]]) -> None:
+        self._allowed_training_algorithms = (
+            None
+            if allowed_model_list is None
+            else [ClassificationModels[camel_to_snake(o)] for o in allowed_model_list]
+        )
+
+    @property
+    def blocked_training_algorithms(self) -> Optional[List]:
+        return self._blocked_training_algorithms
+
+    @blocked_training_algorithms.setter
+    def blocked_training_algorithms(self, blocked_model_list: Union[List[str], List[ClassificationModels]]) -> None:
+        self._blocked_training_algorithms = (
+            None
+            if blocked_model_list is None
+            else [ClassificationModels[camel_to_snake(o)] for o in blocked_model_list]
+        )
+
+    def _to_rest_object(self) -> RestClassificationTrainingSettings:
+        return RestClassificationTrainingSettings(
+            enable_dnn_training=self.enable_dnn_training,
+            enable_onnx_compatible_models=self.enable_onnx_compatible_models,
+            enable_model_explainability=self.enable_model_explainability,
+            enable_stack_ensemble=self.enable_stack_ensemble,
+            enable_vote_ensemble=self.enable_vote_ensemble,
+            stack_ensemble_settings=self.stack_ensemble_settings,
+            ensemble_model_download_timeout=to_iso_duration_format_mins(self.ensemble_model_download_timeout),
+            allowed_training_algorithms=self.allowed_training_algorithms,
+            blocked_training_algorithms=self.blocked_training_algorithms,
+            training_mode=self.training_mode,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestClassificationTrainingSettings) -> "ClassificationTrainingSettings":
+        return cls(
+            enable_dnn_training=obj.enable_dnn_training,
+            enable_onnx_compatible_models=obj.enable_onnx_compatible_models,
+            enable_model_explainability=obj.enable_model_explainability,
+            enable_stack_ensemble=obj.enable_stack_ensemble,
+            enable_vote_ensemble=obj.enable_vote_ensemble,
+            ensemble_model_download_timeout=from_iso_duration_format_mins(obj.ensemble_model_download_timeout),
+            stack_ensemble_settings=obj.stack_ensemble_settings,
+            allowed_training_algorithms=obj.allowed_training_algorithms,
+            blocked_training_algorithms=obj.blocked_training_algorithms,
+            training_mode=obj.training_mode,
+        )
+
+
+class ForecastingTrainingSettings(TrainingSettings):
+    """Forecasting TrainingSettings class for Azure Machine Learning."""
+
+    def __init__(
+        self,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+
+    @property
+    def allowed_training_algorithms(self) -> Optional[List]:
+        return self._allowed_training_algorithms
+
+    @allowed_training_algorithms.setter
+    def allowed_training_algorithms(self, allowed_model_list: Union[List[str], List[ForecastingModels]]) -> None:
+        self._allowed_training_algorithms = (
+            None if allowed_model_list is None else [ForecastingModels[camel_to_snake(o)] for o in allowed_model_list]
+        )
+
+    @property
+    def blocked_training_algorithms(self) -> Optional[List]:
+        return self._blocked_training_algorithms
+
+    @blocked_training_algorithms.setter
+    def blocked_training_algorithms(self, blocked_model_list: Union[List[str], List[ForecastingModels]]) -> None:
+        self._blocked_training_algorithms = (
+            None if blocked_model_list is None else [ForecastingModels[camel_to_snake(o)] for o in blocked_model_list]
+        )
+
+    def _to_rest_object(self) -> RestForecastingTrainingSettings:
+        return RestForecastingTrainingSettings(
+            enable_dnn_training=self.enable_dnn_training,
+            enable_onnx_compatible_models=self.enable_onnx_compatible_models,
+            enable_model_explainability=self.enable_model_explainability,
+            enable_stack_ensemble=self.enable_stack_ensemble,
+            enable_vote_ensemble=self.enable_vote_ensemble,
+            stack_ensemble_settings=self.stack_ensemble_settings,
+            ensemble_model_download_timeout=to_iso_duration_format_mins(self.ensemble_model_download_timeout),
+            allowed_training_algorithms=self.allowed_training_algorithms,
+            blocked_training_algorithms=self.blocked_training_algorithms,
+            training_mode=self.training_mode,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestForecastingTrainingSettings) -> "ForecastingTrainingSettings":
+        return cls(
+            enable_dnn_training=obj.enable_dnn_training,
+            enable_onnx_compatible_models=obj.enable_onnx_compatible_models,
+            enable_model_explainability=obj.enable_model_explainability,
+            enable_stack_ensemble=obj.enable_stack_ensemble,
+            enable_vote_ensemble=obj.enable_vote_ensemble,
+            ensemble_model_download_timeout=from_iso_duration_format_mins(obj.ensemble_model_download_timeout),
+            stack_ensemble_settings=obj.stack_ensemble_settings,
+            allowed_training_algorithms=obj.allowed_training_algorithms,
+            blocked_training_algorithms=obj.blocked_training_algorithms,
+            training_mode=obj.training_mode,
+        )
+
+
+class RegressionTrainingSettings(TrainingSettings):
+    """Regression TrainingSettings class for Azure Machine Learning."""
+
+    def __init__(
+        self,
+        **kwargs: Any,
+    ):
+        super().__init__(**kwargs)
+
+    @property
+    def allowed_training_algorithms(self) -> Optional[List]:
+        return self._allowed_training_algorithms
+
+    @allowed_training_algorithms.setter
+    def allowed_training_algorithms(self, allowed_model_list: Union[List[str], List[ForecastingModels]]) -> None:
+        self._allowed_training_algorithms = (
+            None if allowed_model_list is None else [RegressionModels[camel_to_snake(o)] for o in allowed_model_list]
+        )
+
+    @property
+    def blocked_training_algorithms(self) -> Optional[List]:
+        return self._blocked_training_algorithms
+
+    @blocked_training_algorithms.setter
+    def blocked_training_algorithms(self, blocked_model_list: Union[List[str], List[ForecastingModels]]) -> None:
+        self._blocked_training_algorithms = (
+            None if blocked_model_list is None else [RegressionModels[camel_to_snake(o)] for o in blocked_model_list]
+        )
+
+    def _to_rest_object(self) -> RestRegressionTrainingSettings:
+        return RestRegressionTrainingSettings(
+            enable_dnn_training=self.enable_dnn_training,
+            enable_onnx_compatible_models=self.enable_onnx_compatible_models,
+            enable_model_explainability=self.enable_model_explainability,
+            enable_stack_ensemble=self.enable_stack_ensemble,
+            enable_vote_ensemble=self.enable_vote_ensemble,
+            stack_ensemble_settings=self.stack_ensemble_settings,
+            ensemble_model_download_timeout=to_iso_duration_format_mins(self.ensemble_model_download_timeout),
+            allowed_training_algorithms=self.allowed_training_algorithms,
+            blocked_training_algorithms=self.blocked_training_algorithms,
+            training_mode=self.training_mode,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestRegressionTrainingSettings) -> "RegressionTrainingSettings":
+        return cls(
+            enable_dnn_training=obj.enable_dnn_training,
+            enable_onnx_compatible_models=obj.enable_onnx_compatible_models,
+            enable_model_explainability=obj.enable_model_explainability,
+            enable_stack_ensemble=obj.enable_stack_ensemble,
+            enable_vote_ensemble=obj.enable_vote_ensemble,
+            ensemble_model_download_timeout=from_iso_duration_format_mins(obj.ensemble_model_download_timeout),
+            stack_ensemble_settings=obj.stack_ensemble_settings,
+            allowed_training_algorithms=obj.allowed_training_algorithms,
+            blocked_training_algorithms=obj.blocked_training_algorithms,
+            training_mode=obj.training_mode,
+        )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/utils.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/utils.py
new file mode 100644
index 00000000..08521d7e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/automl/utils.py
@@ -0,0 +1,47 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import TYPE_CHECKING, Dict, Type, Union
+
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._job.automl.image.image_classification_search_space import ImageClassificationSearchSpace
+    from azure.ai.ml.entities._job.automl.image.image_object_detection_search_space import (
+        ImageObjectDetectionSearchSpace,
+    )
+    from azure.ai.ml.entities._job.automl.nlp.nlp_search_space import NlpSearchSpace
+    from azure.ai.ml.entities._job.automl.search_space import SearchSpace
+
+
+def cast_to_specific_search_space(
+    input: Union[Dict, "SearchSpace"],  # pylint: disable=redefined-builtin
+    class_name: Union[
+        Type["ImageClassificationSearchSpace"], Type["ImageObjectDetectionSearchSpace"], Type["NlpSearchSpace"]
+    ],
+    task_type: str,
+) -> Union["ImageClassificationSearchSpace", "ImageObjectDetectionSearchSpace", "NlpSearchSpace"]:
+    def validate_searchspace_args(input_dict: dict) -> None:
+        searchspace = class_name()
+        for key in input_dict:
+            if not hasattr(searchspace, key):
+                msg = f"Received unsupported search space parameter for {task_type} Job."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.AUTOML,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+
+    if isinstance(input, dict):
+        validate_searchspace_args(input)
+        specific_search_space = class_name(**input)
+    else:
+        validate_searchspace_args(input.__dict__)
+        specific_search_space = class_name._from_search_space_object(input)  # pylint: disable=protected-access
+
+    res: Union["ImageClassificationSearchSpace", "ImageObjectDetectionSearchSpace", "NlpSearchSpace"] = (
+        specific_search_space
+    )
+    return res
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/base_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/base_job.py
new file mode 100644
index 00000000..72b464e5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/base_job.py
@@ -0,0 +1,85 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import logging
+from typing import Any, Dict
+
+from azure.ai.ml._restclient.runhistory.models import Run
+from azure.ai.ml._schema.job import BaseJobSchema
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE
+from azure.ai.ml.entities._system_data import SystemData
+from azure.ai.ml.entities._util import load_from_dict
+
+from .job import Job
+
+module_logger = logging.getLogger(__name__)
+
+"""
+TODO[Joe]: This class is temporarily created to handle "Base" job type from the service.
+    We will be working on a more granular job type for pipeline child jobs in the future.
+    Spec Ref: https://github.com/Azure/azureml_run_specification/pull/340
+    MFE PR: https://msdata.visualstudio.com/DefaultCollection/Vienna/_workitems/edit/1167303/
+"""
+
+
+class _BaseJob(Job):
+    """Base Job, only used in pipeline child jobs.
+
+    :param name: Name of the resource.
+    :type name: str
+    :param description: Description of the resource.
+    :type description: str
+    :param tags: Tag dictionary. Tags can be added, removed, and updated.
+    :type tags: dict[str, str]
+    :param properties: The asset property dictionary.
+    :type properties: dict[str, str]
+    :param experiment_name:  Name of the experiment the job will be created under,
+        if None is provided, default will be set to current directory name.
+    :type experiment_name: str
+    :param services: Information on services associated with the job, readonly.
+    :type services: dict[str, JobService]
+    :param compute: The compute target the job runs on.
+    :type compute: str
+    :param kwargs: A dictionary of additional configuration parameters.
+    :type kwargs: dict
+    """
+
+    def __init__(self, **kwargs: Any):
+        kwargs[TYPE] = JobType.BASE
+
+        super().__init__(**kwargs)
+
+    def _to_dict(self) -> Dict:
+        res: dict = BaseJobSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "_BaseJob":
+        loaded_data = load_from_dict(BaseJobSchema, data, context, additional_message, **kwargs)
+        return _BaseJob(**loaded_data)
+
+    @classmethod
+    def _load_from_rest(cls, obj: Run) -> "_BaseJob":
+        creation_context = SystemData(
+            created_by=obj.created_by,
+            created_by_type=obj.created_from,
+            created_at=obj.created_utc,
+            last_modified_by=obj.last_modified_by,
+            last_modified_at=obj.last_modified_utc,
+        )
+        base_job = _BaseJob(
+            name=obj.run_id,
+            display_name=obj.display_name,
+            description=obj.description,
+            tags=obj.tags,
+            properties=obj.properties,
+            experiment_name=obj.experiment_id,
+            services=obj.services,
+            status=obj.status,
+            creation_context=creation_context,
+            compute=f"{obj.compute.target}" if obj.compute else None,
+        )
+
+        return base_job
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/command_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/command_job.py
new file mode 100644
index 00000000..0a0c7e82
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/command_job.py
@@ -0,0 +1,314 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import copy
+import logging
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2025_01_01_preview.models import CommandJob as RestCommandJob
+from azure.ai.ml._restclient.v2025_01_01_preview.models import JobBase
+from azure.ai.ml._schema.job.command_job import CommandJobSchema
+from azure.ai.ml._utils.utils import map_single_brackets_and_warn
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, LOCAL_COMPUTE_PROPERTY, LOCAL_COMPUTE_TARGET, TYPE
+from azure.ai.ml.entities import Environment
+from azure.ai.ml.entities._credentials import (
+    AmlTokenConfiguration,
+    ManagedIdentityConfiguration,
+    UserIdentityConfiguration,
+    _BaseJobIdentityConfiguration,
+)
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._job._input_output_helpers import (
+    from_rest_data_outputs,
+    from_rest_inputs_to_dataset_literal,
+    to_rest_data_outputs,
+    to_rest_dataset_literal_inputs,
+    validate_inputs_for_command,
+)
+from azure.ai.ml.entities._job.distribution import DistributionConfiguration
+from azure.ai.ml.entities._job.job_service import (
+    JobService,
+    JobServiceBase,
+    JupyterLabJobService,
+    SshJobService,
+    TensorBoardJobService,
+    VsCodeJobService,
+)
+from azure.ai.ml.entities._system_data import SystemData
+from azure.ai.ml.entities._util import load_from_dict
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException
+
+from .job import Job
+from .job_io_mixin import JobIOMixin
+from .job_limits import CommandJobLimits
+from .job_resource_configuration import JobResourceConfiguration
+from .parameterized_command import ParameterizedCommand
+from .queue_settings import QueueSettings
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities import CommandComponent
+    from azure.ai.ml.entities._builders import Command
+
+module_logger = logging.getLogger(__name__)
+
+
+class CommandJob(Job, ParameterizedCommand, JobIOMixin):
+    """Command job.
+
+    .. note::
+        For sweep jobs, inputs, outputs, and parameters are accessible as environment variables using the prefix
+        ``AZUREML_PARAMETER_``. For example, if you have a parameter named "input_data", you can access it as
+        ``AZUREML_PARAMETER_input_data``.
+
+    :keyword services: Read-only information on services associated with the job.
+    :paramtype services: Optional[dict[str, ~azure.ai.ml.entities.JobService]]
+    :keyword inputs: Mapping of output data bindings used in the command.
+    :paramtype inputs: Optional[dict[str, Union[~azure.ai.ml.Input, str, bool, int, float]]]
+    :keyword outputs: Mapping of output data bindings used in the job.
+    :paramtype outputs: Optional[dict[str, ~azure.ai.ml.Output]]
+    :keyword identity: The identity that the job will use while running on compute.
+    :paramtype identity: Optional[Union[~azure.ai.ml.ManagedIdentityConfiguration, ~azure.ai.ml.AmlTokenConfiguration,
+        ~azure.ai.ml.UserIdentityConfiguration]]
+    :keyword limits: The limits for the job.
+    :paramtype limits: Optional[~azure.ai.ml.entities.CommandJobLimits]
+    :keyword parent_job_name: parent job id for command job
+    :paramtype parent_job_name: Optional[str]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_command_configurations.py
+            :start-after: [START command_job_definition]
+            :end-before: [END command_job_definition]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a CommandJob.
+    """
+
+    def __init__(
+        self,
+        *,
+        inputs: Optional[Dict[str, Union[Input, str, bool, int, float]]] = None,
+        outputs: Optional[Dict[str, Output]] = None,
+        limits: Optional[CommandJobLimits] = None,
+        identity: Optional[
+            Union[Dict, ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]
+        ] = None,
+        services: Optional[
+            Dict[str, Union[JobService, JupyterLabJobService, SshJobService, TensorBoardJobService, VsCodeJobService]]
+        ] = None,
+        parent_job_name: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        kwargs[TYPE] = JobType.COMMAND
+        self._parameters: dict = kwargs.pop("parameters", {})
+        self.parent_job_name = parent_job_name
+
+        super().__init__(**kwargs)
+
+        self.outputs = outputs  # type: ignore[assignment]
+        self.inputs = inputs  # type: ignore[assignment]
+        self.limits = limits
+        self.identity = identity
+        self.services = services
+
+    @property
+    def parameters(self) -> Dict[str, str]:
+        """MLFlow parameters.
+
+        :return: MLFlow parameters logged in job.
+        :rtype: dict[str, str]
+        """
+        return self._parameters
+
+    def _to_dict(self) -> Dict:
+        res: dict = CommandJobSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def _to_rest_object(self) -> JobBase:
+        self._validate()
+        self.command = map_single_brackets_and_warn(self.command)
+        modified_properties = copy.deepcopy(self.properties)
+        # Remove any properties set on the service as read-only
+        modified_properties.pop("_azureml.ComputeTargetType", None)
+        # Handle local compute case
+        compute = self.compute
+        resources = self.resources
+        if self.compute == LOCAL_COMPUTE_TARGET:
+            compute = None
+            if resources is None:
+                resources = JobResourceConfiguration()
+            if not isinstance(resources, Dict):
+                if resources.properties is None:
+                    resources.properties = {}
+                # This is the format of the October Api response. We need to match it exactly
+                resources.properties[LOCAL_COMPUTE_PROPERTY] = {LOCAL_COMPUTE_PROPERTY: True}
+
+        properties = RestCommandJob(
+            display_name=self.display_name,
+            description=self.description,
+            command=self.command,
+            code_id=self.code,
+            compute_id=compute,
+            properties=modified_properties,
+            experiment_name=self.experiment_name,
+            inputs=to_rest_dataset_literal_inputs(self.inputs, job_type=self.type),
+            outputs=to_rest_data_outputs(self.outputs),
+            environment_id=self.environment,
+            distribution=(
+                self.distribution._to_rest_object()
+                if self.distribution and not isinstance(self.distribution, Dict)
+                else None
+            ),
+            tags=self.tags,
+            identity=(
+                self.identity._to_job_rest_object() if self.identity and not isinstance(self.identity, Dict) else None
+            ),
+            environment_variables=self.environment_variables,
+            resources=resources._to_rest_object() if resources and not isinstance(resources, Dict) else None,
+            limits=self.limits._to_rest_object() if self.limits else None,
+            services=JobServiceBase._to_rest_job_services(self.services),
+            queue_settings=self.queue_settings._to_rest_object() if self.queue_settings else None,
+            parent_job_name=self.parent_job_name,
+        )
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "CommandJob":
+        loaded_data = load_from_dict(CommandJobSchema, data, context, additional_message, **kwargs)
+        return CommandJob(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
+
+    @classmethod
+    def _load_from_rest(cls, obj: JobBase) -> "CommandJob":
+        rest_command_job: RestCommandJob = obj.properties
+        command_job = CommandJob(
+            name=obj.name,
+            id=obj.id,
+            display_name=rest_command_job.display_name,
+            description=rest_command_job.description,
+            tags=rest_command_job.tags,
+            properties=rest_command_job.properties,
+            command=rest_command_job.command,
+            experiment_name=rest_command_job.experiment_name,
+            services=JobServiceBase._from_rest_job_services(rest_command_job.services),
+            status=rest_command_job.status,
+            creation_context=SystemData._from_rest_object(obj.system_data) if obj.system_data else None,
+            code=rest_command_job.code_id,
+            compute=rest_command_job.compute_id,
+            environment=rest_command_job.environment_id,
+            distribution=DistributionConfiguration._from_rest_object(rest_command_job.distribution),
+            parameters=rest_command_job.parameters,
+            # pylint: disable=protected-access
+            identity=(
+                _BaseJobIdentityConfiguration._from_rest_object(rest_command_job.identity)
+                if rest_command_job.identity
+                else None
+            ),
+            environment_variables=rest_command_job.environment_variables,
+            resources=JobResourceConfiguration._from_rest_object(rest_command_job.resources),
+            limits=CommandJobLimits._from_rest_object(rest_command_job.limits),
+            inputs=from_rest_inputs_to_dataset_literal(rest_command_job.inputs),
+            outputs=from_rest_data_outputs(rest_command_job.outputs),
+            queue_settings=QueueSettings._from_rest_object(rest_command_job.queue_settings),
+            parent_job_name=rest_command_job.parent_job_name,
+        )
+        # Handle special case of local job
+        if (
+            command_job.resources is not None
+            and not isinstance(command_job.resources, Dict)
+            and command_job.resources.properties is not None
+            and command_job.resources.properties.get(LOCAL_COMPUTE_PROPERTY, None)
+        ):
+            command_job.compute = LOCAL_COMPUTE_TARGET
+            command_job.resources.properties.pop(LOCAL_COMPUTE_PROPERTY)
+        return command_job
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "CommandComponent":
+        """Translate a command job to component.
+
+        :param context: Context of command job YAML file.
+        :type context: dict
+        :return: Translated command component.
+        :rtype: CommandComponent
+        """
+        from azure.ai.ml.entities import CommandComponent
+
+        pipeline_job_dict = kwargs.get("pipeline_job_dict", {})
+        context = context or {BASE_PATH_CONTEXT_KEY: Path("./")}
+
+        # Create anonymous command component with default version as 1
+        return CommandComponent(
+            tags=self.tags,
+            is_anonymous=True,
+            base_path=context[BASE_PATH_CONTEXT_KEY],
+            code=self.code,
+            command=self.command,
+            environment=self.environment,
+            description=self.description,
+            inputs=self._to_inputs(inputs=self.inputs, pipeline_job_dict=pipeline_job_dict),
+            outputs=self._to_outputs(outputs=self.outputs, pipeline_job_dict=pipeline_job_dict),
+            resources=self.resources if self.resources else None,
+            distribution=self.distribution if self.distribution else None,
+        )
+
+    def _to_node(self, context: Optional[Dict] = None, **kwargs: Any) -> "Command":
+        """Translate a command job to a pipeline node.
+
+        :param context: Context of command job YAML file.
+        :type context: dict
+        :return: Translated command component.
+        :rtype: Command
+        """
+        from azure.ai.ml.entities._builders import Command
+
+        component = self._to_component(context, **kwargs)
+
+        return Command(
+            component=component,
+            compute=self.compute,
+            # Need to supply the inputs with double curly.
+            inputs=self.inputs,  # type: ignore[arg-type]
+            outputs=self.outputs,  # type: ignore[arg-type]
+            environment_variables=self.environment_variables,
+            description=self.description,
+            tags=self.tags,
+            display_name=self.display_name,
+            limits=self.limits,
+            services=self.services,
+            properties=self.properties,
+            identity=self.identity,
+            queue_settings=self.queue_settings,
+        )
+
+    def _validate(self) -> None:
+        if self.command is None:
+            msg = "command is required"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.MISSING_FIELD,
+            )
+        if self.environment is None:
+            msg = "environment is required for non-local runs"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.MISSING_FIELD,
+            )
+        if isinstance(self.environment, Environment):
+            self.environment.validate()
+        validate_inputs_for_command(self.command, self.inputs)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/compute_configuration.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/compute_configuration.py
new file mode 100644
index 00000000..dcc00825
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/compute_configuration.py
@@ -0,0 +1,110 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import json
+import logging
+from typing import Any, Dict, Optional
+
+from azure.ai.ml._restclient.v2020_09_01_dataplanepreview.models import ComputeConfiguration as RestComputeConfiguration
+from azure.ai.ml.constants._common import LOCAL_COMPUTE_TARGET
+from azure.ai.ml.constants._job.job import JobComputePropertyFields
+from azure.ai.ml.entities._mixins import DictMixin, RestTranslatableMixin
+
+module_logger = logging.getLogger(__name__)
+
+
+class ComputeConfiguration(RestTranslatableMixin, DictMixin):
+    """Compute resource configuration
+
+    :param target: The compute target.
+    :type target: Optional[str]
+    :param instance_count: The number of instances.
+    :type instance_count: Optional[int]
+    :param is_local: Specifies if the compute will be on the local machine.
+    :type is_local: Optional[bool]
+    :param location: The location of the compute resource.
+    :type location: Optional[str]
+    :param properties: The resource properties
+    :type properties: Optional[Dict[str, Any]]
+    :param deserialize_properties: Specifies if property bag should be deserialized. Defaults to False.
+    :type deserialize_properties: bool
+    """
+
+    def __init__(
+        self,
+        *,
+        target: Optional[str] = None,
+        instance_count: Optional[int] = None,
+        is_local: Optional[bool] = None,
+        instance_type: Optional[str] = None,
+        location: Optional[str] = None,
+        properties: Optional[Dict[str, Any]] = None,
+        deserialize_properties: bool = False,
+    ) -> None:
+        self.instance_count = instance_count
+        self.target = target or LOCAL_COMPUTE_TARGET
+        self.is_local = is_local or self.target == LOCAL_COMPUTE_TARGET
+        self.instance_type = instance_type
+        self.location = location
+        self.properties = properties
+        if deserialize_properties and properties and self.properties is not None:
+            for key, value in self.properties.items():
+                try:
+                    self.properties[key] = json.loads(value)
+                except Exception:  # pylint: disable=W0718
+                    # keep serialized string if load fails
+                    pass
+
+    def _to_rest_object(self) -> RestComputeConfiguration:
+        if self.properties:
+            serialized_properties = {}
+            for key, value in self.properties.items():
+                try:
+                    if key.lower() == JobComputePropertyFields.SINGULARITY.lower():
+                        # Map Singularity -> AISupercomputer in SDK until MFE does mapping
+                        key = JobComputePropertyFields.AISUPERCOMPUTER
+                    # Ensure keymatch is case invariant
+                    elif key.lower() == JobComputePropertyFields.AISUPERCOMPUTER.lower():
+                        key = JobComputePropertyFields.AISUPERCOMPUTER
+                    serialized_properties[key] = json.dumps(value)
+                except Exception:  # pylint: disable=W0718
+                    pass
+        else:
+            serialized_properties = None
+        return RestComputeConfiguration(
+            target=self.target if not self.is_local else None,
+            is_local=self.is_local,
+            instance_count=self.instance_count,
+            instance_type=self.instance_type,
+            location=self.location,
+            properties=serialized_properties,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestComputeConfiguration) -> "ComputeConfiguration":
+        return ComputeConfiguration(
+            target=obj.target,
+            is_local=obj.is_local,
+            instance_count=obj.instance_count,
+            location=obj.location,
+            instance_type=obj.instance_type,
+            properties=obj.properties,
+            deserialize_properties=True,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ComputeConfiguration):
+            return NotImplemented
+        return (
+            self.instance_count == other.instance_count
+            and self.target == other.target
+            and self.is_local == other.is_local
+            and self.location == other.location
+            and self.instance_type == other.instance_type
+        )
+
+    def __ne__(self, other: object) -> bool:
+        if not isinstance(other, ComputeConfiguration):
+            return NotImplemented
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/data_transfer/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/data_transfer/__init__.py
new file mode 100644
index 00000000..fdf8caba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/data_transfer/__init__.py
@@ -0,0 +1,5 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/data_transfer/data_transfer_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/data_transfer/data_transfer_job.py
new file mode 100644
index 00000000..b510da80
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/data_transfer/data_transfer_job.py
@@ -0,0 +1,358 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import logging
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase
+from azure.ai.ml._schema.job.data_transfer_job import (
+    DataTransferCopyJobSchema,
+    DataTransferExportJobSchema,
+    DataTransferImportJobSchema,
+)
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE
+from azure.ai.ml.constants._component import DataTransferBuiltinComponentUri, DataTransferTaskType, ExternalDataType
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._inputs_outputs.external_data import Database, FileSystem
+from azure.ai.ml.entities._util import load_from_dict
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException
+
+from ..job import Job
+from ..job_io_mixin import JobIOMixin
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._builders import DataTransferCopy, DataTransferExport, DataTransferImport
+    from azure.ai.ml.entities._component.datatransfer_component import DataTransferCopyComponent
+
+module_logger = logging.getLogger(__name__)
+
+
+class DataTransferJob(Job, JobIOMixin):
+    """DataTransfer job.
+
+    :param name: Name of the job.
+    :type name: str
+    :param description: Description of the job.
+    :type description: str
+    :param tags: Tag dictionary. Tags can be added, removed, and updated.
+    :type tags: dict[str, str]
+    :param display_name: Display name of the job.
+    :type display_name: str
+    :param properties: The asset property dictionary.
+    :type properties: dict[str, str]
+    :param experiment_name:  Name of the experiment the job will be created under.
+        If None is provided, default will be set to current directory name.
+    :type experiment_name: str
+    :param services: Information on services associated with the job, readonly.
+    :type services: dict[str, JobService]
+    :param inputs: Inputs to the command.
+    :type inputs: dict[str, Union[azure.ai.ml.Input, str, bool, int, float]]
+    :param outputs: Mapping of output data bindings used in the job.
+    :type outputs: dict[str, azure.ai.ml.Output]
+    :param compute: The compute target the job runs on.
+    :type compute: str
+    :param task: task type in data transfer component, possible value is "copy_data".
+    :type task: str
+    :param data_copy_mode: data copy mode in copy task, possible value is "merge_with_overwrite", "fail_if_conflict".
+    :type data_copy_mode: str
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+    """
+
+    def __init__(
+        self,
+        task: str,
+        **kwargs: Any,
+    ):
+        kwargs[TYPE] = JobType.DATA_TRANSFER
+        self._parameters: Dict = kwargs.pop("parameters", {})
+        super().__init__(**kwargs)
+        self.task = task
+
+    @property
+    def parameters(self) -> Dict:
+        """MLFlow parameters.
+
+        :return: MLFlow parameters logged in job.
+        :rtype: Dict[str, str]
+        """
+        return self._parameters
+
+    def _validate(self) -> None:
+        if self.compute is None:
+            msg = "compute is required"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.MISSING_FIELD,
+            )
+
+    @classmethod
+    def _load_from_rest(cls, obj: JobBase) -> "DataTransferJob":
+        # Todo: need update rest api
+        raise NotImplementedError("Not support submit standalone job for now")
+
+    def _to_rest_object(self) -> JobBase:
+        # Todo: need update rest api
+        raise NotImplementedError("Not support submit standalone job for now")
+
+    @classmethod
+    def _build_source_sink(
+        cls, io_dict: Optional[Union[Dict, Database, FileSystem]]
+    ) -> Optional[Union[(Database, FileSystem)]]:
+        if io_dict is None:
+            return io_dict
+        if isinstance(io_dict, (Database, FileSystem)):
+            component_io = io_dict
+        else:
+            if isinstance(io_dict, dict):
+                data_type = io_dict.pop("type", None)
+                if data_type == ExternalDataType.DATABASE:
+                    component_io = Database(**io_dict)
+                elif data_type == ExternalDataType.FILE_SYSTEM:
+                    component_io = FileSystem(**io_dict)
+                else:
+                    msg = "Type in source or sink only support {} and {}, currently got {}."
+                    raise ValidationException(
+                        message=msg.format(
+                            ExternalDataType.DATABASE,
+                            ExternalDataType.FILE_SYSTEM,
+                            data_type,
+                        ),
+                        no_personal_data_message=msg.format(
+                            ExternalDataType.DATABASE,
+                            ExternalDataType.FILE_SYSTEM,
+                            "data_type",
+                        ),
+                        target=ErrorTarget.DATA_TRANSFER_JOB,
+                        error_category=ErrorCategory.USER_ERROR,
+                        error_type=ValidationErrorType.INVALID_VALUE,
+                    )
+            else:
+                msg = "Source or sink only support dict, Database and FileSystem"
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.DATA_TRANSFER_JOB,
+                    error_category=ErrorCategory.USER_ERROR,
+                    error_type=ValidationErrorType.INVALID_VALUE,
+                )
+
+        return component_io
+
+
+class DataTransferCopyJob(DataTransferJob):
+    def __init__(
+        self,
+        *,
+        inputs: Optional[Dict[str, Union[Input, str]]] = None,
+        outputs: Optional[Dict[str, Union[Output]]] = None,
+        data_copy_mode: Optional[str] = None,
+        **kwargs: Any,
+    ):
+        kwargs["task"] = DataTransferTaskType.COPY_DATA
+        super().__init__(**kwargs)
+
+        self.outputs = outputs  # type: ignore[assignment]
+        self.inputs = inputs  # type: ignore[assignment]
+        self.data_copy_mode = data_copy_mode
+
+    def _to_dict(self) -> Dict:
+        res: dict = DataTransferCopyJobSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    @classmethod
+    def _load_from_dict(
+        cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any
+    ) -> "DataTransferCopyJob":
+        loaded_data = load_from_dict(DataTransferCopyJobSchema, data, context, additional_message, **kwargs)
+        return DataTransferCopyJob(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "DataTransferCopyComponent":
+        """Translate a data transfer copy job to component.
+
+        :param context: Context of data transfer job YAML file.
+        :type context: dict
+        :return: Translated data transfer copy component.
+        :rtype: DataTransferCopyComponent
+        """
+        from azure.ai.ml.entities._component.datatransfer_component import DataTransferCopyComponent
+
+        pipeline_job_dict = kwargs.get("pipeline_job_dict", {})
+        context = context or {BASE_PATH_CONTEXT_KEY: Path("./")}
+
+        # Create anonymous command component with default version as 1
+        return DataTransferCopyComponent(
+            tags=self.tags,
+            is_anonymous=True,
+            base_path=context[BASE_PATH_CONTEXT_KEY],
+            description=self.description,
+            inputs=self._to_inputs(inputs=self.inputs, pipeline_job_dict=pipeline_job_dict),
+            outputs=self._to_outputs(outputs=self.outputs, pipeline_job_dict=pipeline_job_dict),
+            data_copy_mode=self.data_copy_mode,
+        )
+
+    def _to_node(self, context: Optional[Dict] = None, **kwargs: Any) -> "DataTransferCopy":
+        """Translate a data transfer copy job to a pipeline node.
+
+        :param context: Context of data transfer job YAML file.
+        :type context: dict
+        :return: Translated data transfer component.
+        :rtype: DataTransferCopy
+        """
+        from azure.ai.ml.entities._builders import DataTransferCopy
+
+        component = self._to_component(context, **kwargs)
+
+        return DataTransferCopy(
+            component=component,
+            compute=self.compute,
+            # Need to supply the inputs with double curly.
+            inputs=self.inputs,  # type: ignore[arg-type]
+            outputs=self.outputs,  # type: ignore[arg-type]
+            description=self.description,
+            tags=self.tags,
+            display_name=self.display_name,
+        )
+
+
+class DataTransferImportJob(DataTransferJob):
+    def __init__(
+        self,
+        *,
+        outputs: Optional[Dict[str, Union[Output]]] = None,
+        source: Optional[Union[Dict, Database, FileSystem]] = None,
+        **kwargs: Any,
+    ):
+        kwargs["task"] = DataTransferTaskType.IMPORT_DATA
+        super().__init__(**kwargs)
+
+        self.outputs = outputs  # type: ignore[assignment]
+        self.source = self._build_source_sink(source)
+
+    def _to_dict(self) -> Dict:
+        res: dict = DataTransferImportJobSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    @classmethod
+    def _load_from_dict(
+        cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any
+    ) -> "DataTransferImportJob":
+        loaded_data = load_from_dict(DataTransferImportJobSchema, data, context, additional_message, **kwargs)
+        return DataTransferImportJob(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> str:
+        """Translate a data transfer import job to component.
+
+        :param context: Context of data transfer job YAML file.
+        :type context: dict
+        :return: Translated data transfer import component.
+        :rtype: str
+        """
+
+        component: str = ""
+        if self.source is not None and self.source.type == ExternalDataType.DATABASE:
+            component = DataTransferBuiltinComponentUri.IMPORT_DATABASE
+        else:
+            component = DataTransferBuiltinComponentUri.IMPORT_FILE_SYSTEM
+
+        return component
+
+    def _to_node(self, context: Optional[Dict] = None, **kwargs: Any) -> "DataTransferImport":
+        """Translate a data transfer import job to a pipeline node.
+
+        :param context: Context of data transfer job YAML file.
+        :type context: dict
+        :return: Translated data transfer import node.
+        :rtype: DataTransferImport
+        """
+        from azure.ai.ml.entities._builders import DataTransferImport
+
+        component = self._to_component(context, **kwargs)
+
+        return DataTransferImport(
+            component=component,
+            compute=self.compute,
+            source=self.source,
+            outputs=self.outputs,  # type: ignore[arg-type]
+            description=self.description,
+            tags=self.tags,
+            display_name=self.display_name,
+            properties=self.properties,
+        )
+
+
+class DataTransferExportJob(DataTransferJob):
+    def __init__(
+        self,
+        *,
+        inputs: Optional[Dict[str, Union[Input]]] = None,
+        sink: Optional[Union[Dict, Database, FileSystem]] = None,
+        **kwargs: Any,
+    ):
+        kwargs["task"] = DataTransferTaskType.EXPORT_DATA
+        super().__init__(**kwargs)
+
+        self.inputs = inputs  # type: ignore[assignment]
+        self.sink = self._build_source_sink(sink)
+
+    def _to_dict(self) -> Dict:
+        res: dict = DataTransferExportJobSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    @classmethod
+    def _load_from_dict(
+        cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any
+    ) -> "DataTransferExportJob":
+        loaded_data = load_from_dict(DataTransferExportJobSchema, data, context, additional_message, **kwargs)
+        return DataTransferExportJob(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> str:
+        """Translate a data transfer export job to component.
+
+        :param context: Context of data transfer job YAML file.
+        :type context: dict
+        :return: Translated data transfer export component.
+        :rtype: str
+        """
+        component: str = ""
+        if self.sink is not None and self.sink.type == ExternalDataType.DATABASE:
+            component = DataTransferBuiltinComponentUri.EXPORT_DATABASE
+        else:
+            msg = "Sink is a required field for export data task and we don't support exporting file system for now."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.DATA_TRANSFER_JOB,
+                error_type=ValidationErrorType.INVALID_VALUE,
+            )
+        return component
+
+    def _to_node(self, context: Optional[Dict] = None, **kwargs: Any) -> "DataTransferExport":
+        """Translate a data transfer export job to a pipeline node.
+
+        :param context: Context of data transfer job YAML file.
+        :type context: dict
+        :return: Translated data transfer export node.
+        :rtype: DataTransferExport
+        """
+        from azure.ai.ml.entities._builders import DataTransferExport
+
+        component = self._to_component(context, **kwargs)
+
+        return DataTransferExport(
+            component=component,
+            compute=self.compute,
+            sink=self.sink,
+            inputs=self.inputs,  # type: ignore[arg-type]
+            description=self.description,
+            tags=self.tags,
+            display_name=self.display_name,
+            properties=self.properties,
+        )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/__init__.py
new file mode 100644
index 00000000..fdf8caba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/__init__.py
@@ -0,0 +1,5 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/constants.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/constants.py
new file mode 100644
index 00000000..5084ffbd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/constants.py
@@ -0,0 +1,20 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+
+class AzureMLDistillationProperties:
+    ENABLE_DISTILLATION = "azureml.enable_distillation"
+    DATA_GENERATION_TYPE = "azureml.data_generation_type"
+    DATA_GENERATION_TASK_TYPE = "azureml.data_generation_task_type"
+    TEACHER_MODEL = "azureml.teacher_model"
+    INSTANCE_TYPE = "azureml.instance_type"
+    CONNECTION_INFORMATION = "azureml.connection_information"
+
+
+class EndpointSettings:
+    VALID_SETTINGS = {"request_batch_size", "min_endpoint_success_ratio"}
+
+
+class PromptSettingKeys:
+    VALID_SETTINGS = {"enable_chain_of_thought", "enable_chain_of_density", "max_len_summary"}
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/distillation_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/distillation_job.py
new file mode 100644
index 00000000..469fde98
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/distillation_job.py
@@ -0,0 +1,542 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import json
+from typing import Any, Dict, Optional
+
+from azure.ai.ml._restclient.v2024_01_01_preview.models import (
+    CustomModelFineTuning as RestCustomModelFineTuningVertical,
+)
+from azure.ai.ml._restclient.v2024_01_01_preview.models import FineTuningJob as RestFineTuningJob
+from azure.ai.ml._restclient.v2024_01_01_preview.models import JobBase as RestJobBase
+from azure.ai.ml._restclient.v2024_01_01_preview.models import MLFlowModelJobInput, UriFileJobInput
+from azure.ai.ml._utils._experimental import experimental
+from azure.ai.ml.constants import DataGenerationType, JobType
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE, AssetTypes
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+from azure.ai.ml.entities._job.distillation.constants import (
+    AzureMLDistillationProperties,
+    EndpointSettings,
+    PromptSettingKeys,
+)
+from azure.ai.ml.entities._job.distillation.endpoint_request_settings import EndpointRequestSettings
+from azure.ai.ml.entities._job.distillation.prompt_settings import PromptSettings
+from azure.ai.ml.entities._job.distillation.teacher_model_settings import TeacherModelSettings
+from azure.ai.ml.entities._job.job import Job
+from azure.ai.ml.entities._job.job_io_mixin import JobIOMixin
+from azure.ai.ml.entities._job.resource_configuration import ResourceConfiguration
+from azure.ai.ml.entities._util import load_from_dict
+from azure.ai.ml.entities._workspace.connections.workspace_connection import WorkspaceConnection
+
+
+# pylint: disable=too-many-instance-attributes
+@experimental
+class DistillationJob(Job, JobIOMixin):
+    def __init__(
+        self,
+        *,
+        data_generation_type: str,
+        data_generation_task_type: str,
+        teacher_model_endpoint_connection: WorkspaceConnection,
+        student_model: Input,
+        training_data: Optional[Input] = None,
+        validation_data: Optional[Input] = None,
+        teacher_model_settings: Optional[TeacherModelSettings] = None,
+        prompt_settings: Optional[PromptSettings] = None,
+        hyperparameters: Optional[Dict] = None,
+        resources: Optional[ResourceConfiguration] = None,
+        **kwargs: Any,
+    ) -> None:
+        self._data_generation_type = data_generation_type
+        self._data_generation_task_type = data_generation_task_type
+        self._teacher_model_endpoint_connection = teacher_model_endpoint_connection
+        self._student_model = student_model
+        self._training_data = training_data
+        self._validation_data = validation_data
+        self._teacher_model_settings = teacher_model_settings
+        self._prompt_settings = prompt_settings
+        self._hyperparameters = hyperparameters
+        self._resources = resources
+
+        if self._training_data is None and self._data_generation_type == DataGenerationType.LABEL_GENERATION:
+            raise ValueError(
+                f"Training data can not be None when data generation type is set to "
+                f"{DataGenerationType.LABEL_GENERATION}."
+            )
+
+        if self._validation_data is None and self._data_generation_type == DataGenerationType.LABEL_GENERATION:
+            raise ValueError(
+                f"Validation data can not be None when data generation type is set to "
+                f"{DataGenerationType.LABEL_GENERATION}."
+            )
+
+        kwargs[TYPE] = JobType.DISTILLATION
+        self._outputs = kwargs.pop("outputs", None)
+        super().__init__(**kwargs)
+
+    @property
+    def data_generation_type(self) -> str:
+        """Get the type of synthetic data generation to perform.
+
+        :return: str representing the type of synthetic data generation to perform.
+        :rtype: str
+        """
+        return self._data_generation_type
+
+    @data_generation_type.setter
+    def data_generation_type(self, task: str) -> None:
+        """Set the data generation task.
+
+        :param task: The data generation task. Possible values include 'Label_Generation' and 'Data_Generation'.
+        :type task: str
+        """
+        self._data_generation_type = task
+
+    @property
+    def data_generation_task_type(self) -> str:
+        """Get the type of synthetic data to generate.
+
+        :return: str representing the type of synthetic data to generate.
+        :rtype: str
+        """
+        return self._data_generation_task_type
+
+    @data_generation_task_type.setter
+    def data_generation_task_type(self, task: str) -> None:
+        """Set the data generation type.
+
+        :param task: The data generation type. Possible values include 'nli', 'nlu_qa', 'conversational',
+                     'math', and 'summarization'.
+        :type task: str
+        """
+        self._data_generation_task_type = task
+
+    @property
+    def teacher_model_endpoint_connection(self) -> WorkspaceConnection:
+        """Get the endpoint connection of the teacher model to use for data generation.
+
+        :return: Endpoint connection
+        :rtype: WorkspaceConnection
+        """
+        return self._teacher_model_endpoint_connection
+
+    @teacher_model_endpoint_connection.setter
+    def teacher_model_endpoint_connection(self, connection: WorkspaceConnection) -> None:
+        """Set the endpoint information of the teacher model.
+
+        :param connection: Workspace connection
+        :type connection: WorkspaceConnection
+        """
+        self._teacher_model_endpoint_connection = connection
+
+    @property
+    def student_model(self) -> Input:
+        """Get the student model to be trained with synthetic data
+
+        :return: The student model to be finetuned
+        :rtype: Input
+        """
+        return self._student_model
+
+    @student_model.setter
+    def student_model(self, model: Input) -> None:
+        """Set the student model to be trained.
+
+        :param model: The model to use for finetuning
+        :type model: Input
+        """
+        self._student_model = model
+
+    @property
+    def training_data(self) -> Optional[Input]:
+        """Get the training data.
+
+        :return: Training data input
+        :rtype: typing.Optional[Input]
+        """
+        return self._training_data
+
+    @training_data.setter
+    def training_data(self, training_data: Optional[Input]) -> None:
+        """Set the training data.
+
+        :param training_data: Training data input
+        :type training_data: typing.Optional[Input]
+        """
+        self._training_data = training_data
+
+    @property
+    def validation_data(self) -> Optional[Input]:
+        """Get the validation data.
+
+        :return: Validation data input
+        :rtype: typing.Optional[Input]
+        """
+        return self._validation_data
+
+    @validation_data.setter
+    def validation_data(self, validation_data: Optional[Input]) -> None:
+        """Set the validation data.
+
+        :param validation_data: Validation data input
+        :type validation_data: typing.Optional[Input]
+        """
+        self._validation_data = validation_data
+
+    @property
+    def teacher_model_settings(self) -> Optional[TeacherModelSettings]:
+        """Get the teacher model settings.
+
+        :return: The settings for the teacher model to use.
+        :rtype: typing.Optional[TeacherModelSettings]
+        """
+        return self._teacher_model_settings
+
+    @property
+    def prompt_settings(self) -> Optional[PromptSettings]:
+        """Get the settings for the prompt.
+
+        :return: The settings for the prompt.
+        :rtype: typing.Optional[PromptSettings]
+        """
+        return self._prompt_settings
+
+    @property
+    def hyperparameters(self) -> Optional[Dict]:
+        """Get the finetuning hyperparameters.
+
+        :return: The finetuning hyperparameters.
+        :rtype: typing.Optional[typing.Dict]
+        """
+        return self._hyperparameters
+
+    @property
+    def resources(self) -> Optional[ResourceConfiguration]:
+        """Get the resources for data generation.
+
+        :return: The resources for data generation.
+        :rtype: typing.Optional[ResourceConfiguration]
+        """
+        return self._resources
+
+    @resources.setter
+    def resources(self, resource: Optional[ResourceConfiguration]) -> None:
+        """Set the resources for data generation.
+
+        :param resource: The resources for data generation.
+        :type resource: typing.Optional[ResourceConfiguration]
+        """
+        self._resources = resource
+
+    def set_teacher_model_settings(
+        self,
+        inference_parameters: Optional[Dict] = None,
+        endpoint_request_settings: Optional[EndpointRequestSettings] = None,
+    ):
+        """Set settings related to the teacher model.
+
+        :param inference_parameters: Settings the teacher model uses during inferencing.
+        :type inference_parameters: typing.Optional[typing.Dict]
+        :param endpoint_request_settings: Settings for inference requests to the endpoint
+        :type endpoint_request_settings: typing.Optional[EndpointRequestSettings]
+        """
+        self._teacher_model_settings = TeacherModelSettings(
+            inference_parameters=inference_parameters, endpoint_request_settings=endpoint_request_settings
+        )
+
+    def set_prompt_settings(self, prompt_settings: Optional[PromptSettings]):
+        """Set settings related to the system prompt used for generating data.
+
+        :param prompt_settings: Settings related to the system prompt used for generating data.
+        :type prompt_settings: typing.Optional[PromptSettings]
+        """
+        self._prompt_settings = prompt_settings if prompt_settings is not None else self._prompt_settings
+
+    def set_finetuning_settings(self, hyperparameters: Optional[Dict]):
+        """Set the hyperparamters for finetuning.
+
+        :param hyperparameters: The hyperparameters for finetuning.
+        :type hyperparameters: typing.Optional[typing.Dict]
+        """
+        self._hyperparameters = hyperparameters if hyperparameters is not None else self._hyperparameters
+
+    def _to_dict(self) -> Dict:
+        """Convert the object to a dictionary.
+
+        :return: dictionary representation of the object.
+        :rtype: typing.Dict
+        """
+        from azure.ai.ml._schema._distillation.distillation_job import DistillationJobSchema
+
+        schema_dict: dict = {}
+        schema_dict = DistillationJobSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+
+        return schema_dict
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "DistillationJob":
+        """Load from a dictionary.
+
+        :param data: dictionary representation of the object.
+        :type data: typing.Dict
+        :param context: dictionary containing the context.
+        :type context: typing.Dict
+        :param additional_message: additional message to be added to the error message.
+        :type additional_message: str
+        :return: DistillationJob object.
+        :rtype: DistillationJob
+        """
+        from azure.ai.ml._schema._distillation.distillation_job import DistillationJobSchema
+
+        loaded_data = load_from_dict(DistillationJobSchema, data, context, additional_message, **kwargs)
+
+        training_data = loaded_data.get("training_data", None)
+        if isinstance(training_data, str):
+            loaded_data["training_data"] = Input(type="uri_file", path=training_data)
+
+        validation_data = loaded_data.get("validation_data", None)
+        if isinstance(validation_data, str):
+            loaded_data["validation_data"] = Input(type="uri_file", path=validation_data)
+
+        student_model = loaded_data.get("student_model", None)
+        if isinstance(student_model, str):
+            loaded_data["student_model"] = Input(type=AssetTypes.URI_FILE, path=student_model)
+
+        job_instance = DistillationJob(**loaded_data)
+        return job_instance
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestJobBase) -> "DistillationJob":
+        """Convert a REST object to DistillationJob object.
+
+        :param obj: CustomModelFineTuningJob in Rest format.
+        :type obj: JobBase
+        :return: DistillationJob objects.
+        :rtype: DistillationJob
+        """
+        properties: RestFineTuningJob = obj.properties
+        finetuning_details: RestCustomModelFineTuningVertical = properties.fine_tuning_details
+
+        job_kwargs_dict = DistillationJob._filter_properties(properties=properties.properties)
+
+        job_args_dict = {
+            "id": obj.id,
+            "name": obj.name,
+            "description": properties.description,
+            "tags": properties.tags,
+            "properties": properties.properties,
+            "experiment_name": properties.experiment_name,
+            "services": properties.services,
+            "status": properties.status,
+            "creation_context": obj.system_data,
+            "display_name": properties.display_name,
+            "outputs": from_rest_data_outputs(properties.outputs),
+        }
+
+        distillation_job = cls(
+            student_model=finetuning_details.model,
+            training_data=finetuning_details.training_data,
+            validation_data=finetuning_details.validation_data,
+            hyperparameters=finetuning_details.hyper_parameters,
+            **job_kwargs_dict,
+            **job_args_dict,
+        )
+
+        distillation_job._restore_inputs()
+
+        return distillation_job
+
+    def _to_rest_object(self) -> "RestFineTuningJob":
+        """Convert DistillationJob object to a RestFineTuningJob object.
+
+        :return: REST object representation of this object.
+        :rtype: JobBase
+        """
+        distillation = RestCustomModelFineTuningVertical(
+            task_type="ChatCompletion",
+            model=self.student_model,
+            model_provider="Custom",
+            training_data=self.training_data,
+            validation_data=self.validation_data,
+            hyper_parameters=self._hyperparameters,
+        )
+
+        if isinstance(distillation.training_data, Input):
+            distillation.training_data = UriFileJobInput(uri=distillation.training_data.path)
+        if isinstance(distillation.validation_data, Input):
+            distillation.validation_data = UriFileJobInput(uri=distillation.validation_data.path)
+        if isinstance(distillation.model, Input):
+            distillation.model = MLFlowModelJobInput(uri=distillation.model.path)
+
+        self._add_distillation_properties(self.properties)
+
+        finetuning_job = RestFineTuningJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            services=self.services,
+            tags=self.tags,
+            properties=self.properties,
+            fine_tuning_details=distillation,
+            outputs=to_rest_data_outputs(self.outputs),
+        )
+
+        result = RestJobBase(properties=finetuning_job)
+        result.name = self.name
+
+        return result
+
+    @classmethod
+    def _load_from_rest(cls, obj: RestJobBase) -> "DistillationJob":
+        """Loads the rest object to a dict containing items to init the AutoMLJob objects.
+
+        :param obj: Azure Resource Manager resource envelope.
+        :type obj: JobBase
+        :raises ValidationException: task type validation error
+        :return: A DistillationJob
+        :rtype: DistillationJob
+        """
+        return DistillationJob._from_rest_object(obj)
+
+    # TODO: Remove once Distillation is added to MFE
+    def _add_distillation_properties(self, properties: Dict) -> None:
+        """Adds DistillationJob attributes to properties to pass into the FT Overloaded API property bag
+
+        :param properties: Current distillation properties
+        :type properties: typing.Dict
+        """
+        properties[AzureMLDistillationProperties.ENABLE_DISTILLATION] = True
+        properties[AzureMLDistillationProperties.DATA_GENERATION_TASK_TYPE] = self._data_generation_task_type.upper()
+        properties[f"{AzureMLDistillationProperties.TEACHER_MODEL}.endpoint_name"] = (
+            self._teacher_model_endpoint_connection.name
+        )
+
+        # Not needed for FT Overload API but additional info needed to convert from REST object to Distillation object
+        properties[AzureMLDistillationProperties.DATA_GENERATION_TYPE] = self._data_generation_type
+        properties[AzureMLDistillationProperties.CONNECTION_INFORMATION] = json.dumps(
+            self._teacher_model_endpoint_connection._to_dict()  # pylint: disable=protected-access
+        )
+
+        if self._prompt_settings:
+            for setting, value in self._prompt_settings.items():
+                if value is not None:
+                    properties[f"azureml.{setting.strip('_')}"] = value
+
+        if self._teacher_model_settings:
+            inference_settings = self._teacher_model_settings.inference_parameters
+            endpoint_settings = self._teacher_model_settings.endpoint_request_settings
+
+            if inference_settings:
+                for inference_key, value in inference_settings.items():
+                    if value is not None:
+                        properties[f"{AzureMLDistillationProperties.TEACHER_MODEL}.{inference_key}"] = value
+
+            if endpoint_settings:
+                for setting, value in endpoint_settings.items():
+                    if value is not None:
+                        properties[f"azureml.{setting.strip('_')}"] = value
+
+        if self._resources and self._resources.instance_type:
+            properties[f"{AzureMLDistillationProperties.INSTANCE_TYPE}.data_generation"] = self._resources.instance_type
+
+    # TODO: Remove once Distillation is added to MFE
+    @classmethod
+    def _filter_properties(cls, properties: Dict) -> Dict:
+        """Convert properties from REST object back to their original states.
+
+        :param properties: Properties from a REST object
+        :type properties: typing.Dict
+        :return: A dict that can be used to create a DistillationJob
+        :rtype: typing.Dict
+        """
+        inference_parameters = {}
+        endpoint_settings = {}
+        prompt_settings = {}
+        resources = {}
+        teacher_settings = {}
+        teacher_model_info = ""
+        for key, val in properties.items():
+            param = key.split(".")[-1]
+            if AzureMLDistillationProperties.TEACHER_MODEL in key and param != "endpoint_name":
+                inference_parameters[param] = val
+            elif AzureMLDistillationProperties.INSTANCE_TYPE in key:
+                resources[key.split(".")[1]] = val
+            elif AzureMLDistillationProperties.CONNECTION_INFORMATION in key:
+                teacher_model_info = val
+            else:
+                if param in EndpointSettings.VALID_SETTINGS:
+                    endpoint_settings[param] = val
+                elif param in PromptSettingKeys.VALID_SETTINGS:
+                    prompt_settings[param] = val
+
+        if inference_parameters:
+            teacher_settings["inference_parameters"] = inference_parameters
+        if endpoint_settings:
+            teacher_settings["endpoint_request_settings"] = EndpointRequestSettings(**endpoint_settings)  # type: ignore
+
+        return {
+            "data_generation_task_type": properties.get(AzureMLDistillationProperties.DATA_GENERATION_TASK_TYPE),
+            "data_generation_type": properties.get(AzureMLDistillationProperties.DATA_GENERATION_TYPE),
+            "teacher_model_endpoint_connection": WorkspaceConnection._load(  # pylint: disable=protected-access
+                data=json.loads(teacher_model_info)
+            ),
+            "teacher_model_settings": (
+                TeacherModelSettings(**teacher_settings) if teacher_settings else None  # type: ignore
+            ),
+            "prompt_settings": PromptSettings(**prompt_settings) if prompt_settings else None,
+            "resources": ResourceConfiguration(**resources) if resources else None,
+        }
+
+    def _restore_inputs(self) -> None:
+        """Restore UriFileJobInputs to JobInputs within data_settings."""
+        if isinstance(self.training_data, UriFileJobInput):
+            self.training_data = Input(type=AssetTypes.URI_FILE, path=self.training_data.uri)
+        if isinstance(self.validation_data, UriFileJobInput):
+            self.validation_data = Input(type=AssetTypes.URI_FILE, path=self.validation_data.uri)
+        if isinstance(self.student_model, MLFlowModelJobInput):
+            self.student_model = Input(type=AssetTypes.MLFLOW_MODEL, path=self.student_model.uri)
+
+    def __eq__(self, other: object) -> bool:
+        """Returns True if both instances have the same values.
+
+        This method check instances equality and returns True if both of
+        the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, DistillationJob):
+            return False
+        return (
+            super().__eq__(other)
+            and self.data_generation_type == other.data_generation_type
+            and self.data_generation_task_type == other.data_generation_task_type
+            and self.teacher_model_endpoint_connection.name == other.teacher_model_endpoint_connection.name
+            and self.student_model == other.student_model
+            and self.training_data == other.training_data
+            and self.validation_data == other.validation_data
+            and self.teacher_model_settings == other.teacher_model_settings
+            and self.prompt_settings == other.prompt_settings
+            and self.hyperparameters == other.hyperparameters
+            and self.resources == other.resources
+        )
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two DistillationJob objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/endpoint_request_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/endpoint_request_settings.py
new file mode 100644
index 00000000..89fb8015
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/endpoint_request_settings.py
@@ -0,0 +1,90 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from typing import Optional
+
+from azure.ai.ml._utils._experimental import experimental
+
+
+@experimental
+class EndpointRequestSettings:
+    def __init__(self, *, request_batch_size: Optional[int] = None, min_endpoint_success_ratio: Optional[float] = None):
+        """Initialize EndpointRequestSettings.
+
+        :param request_batch_size: The number of requests to send to the teacher model endpoint as a batch,
+            defaults to None
+        :type request_batch_size: typing.Optional[int], optional
+        :param min_endpoint_success_ratio: The ratio of (successful requests / total requests) needed for the
+            data generation step to be considered successful. Must be a value between 0 and 1 inclusive,
+            defaults to None
+        :type min_endpoint_success_ratio: typing.Optional[float], optional
+        """
+        self._request_batch_size = request_batch_size
+        self._min_endpoint_success_ratio = min_endpoint_success_ratio
+
+    @property
+    def request_batch_size(self) -> Optional[int]:
+        """Get the number of inference requests to send to the teacher model as a batch.
+
+        :return: The number of inference requests to send to the teacher model as a batch.
+        :rtype: typing.Optional[int]
+        """
+        return self._request_batch_size
+
+    @request_batch_size.setter
+    def request_batch_size(self, value: Optional[int]) -> None:
+        """Set the number of inference requests to send to the teacher model as a batch.
+
+        :param value: The number of inference requests to send to the teacher model as a batch.
+        :type value: typing.Optional[int]
+        """
+        self._request_batch_size = value
+
+    @property
+    def min_endpoint_success_ratio(self) -> Optional[float]:
+        """Get the minimum ratio of successful inferencing requests.
+
+        :return: The minimum ratio of successful inferencing requests.
+        :rtype: typing.Optional[float]
+        """
+        return self._min_endpoint_success_ratio
+
+    @min_endpoint_success_ratio.setter
+    def min_endpoint_success_ratio(self, ratio: Optional[float]) -> None:
+        """Set the minimum ratio of successful inferencing requests.
+
+        :param ratio: The minimum ratio of successful inferencing requests.
+        :type ratio: typing.Optional[float]
+        """
+        self._min_endpoint_success_ratio = ratio
+
+    def items(self):
+        return self.__dict__.items()
+
+    def __eq__(self, other: object) -> bool:
+        """Returns True if both instances have the same values.
+
+        This method check instances equality and returns True if both of
+            the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, EndpointRequestSettings):
+            return False
+        return (
+            self.request_batch_size == other.request_batch_size
+            and self.min_endpoint_success_ratio == other.min_endpoint_success_ratio
+        )
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two EndpointRequestSettings objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/prompt_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/prompt_settings.py
new file mode 100644
index 00000000..d74af748
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/prompt_settings.py
@@ -0,0 +1,138 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from typing import Optional
+
+from azure.ai.ml._utils._experimental import experimental
+
+
+@experimental
+class PromptSettings:
+    def __init__(
+        self,
+        *,
+        enable_chain_of_thought: bool = False,
+        enable_chain_of_density: bool = False,
+        max_len_summary: Optional[int] = None,
+        # custom_prompt: Optional[str] = None
+    ):
+        """Initialize PromptSettings.
+
+        :param enable_chain_of_thought: Whether or not to enable chain of thought which modifies the system prompt
+            used. Can be used for all `data_generation_task_type` values except `SUMMARIZATION`, defaults to False
+        :type enable_chain_of_thought: bool, optional
+        :param enable_chain_of_density: Whether or not to enable chain of density which modifies the system prompt
+            used. Can only be used for `data_generation_task_type` of `SUMMARIZATION`, defaults to False
+        :type enable_chain_of_density: bool, optional
+        :param max_len_summary: The maximum length of the summary generated for data_generation_task_type` of
+            `SUMMARIZATION`, defaults to None
+        :type max_len_summary: typing.Optional[int]
+        """
+        self._enable_chain_of_thought = enable_chain_of_thought
+        self._enable_chain_of_density = enable_chain_of_density
+        self._max_len_summary = max_len_summary
+        # self._custom_prompt = custom_prompt
+
+    @property
+    def enable_chain_of_thought(self) -> bool:
+        """Get whether or not chain of thought is enabled.
+
+        :return: Whether or not chain of thought is enabled.
+        :rtype: bool
+        """
+        return self._enable_chain_of_thought
+
+    @enable_chain_of_thought.setter
+    def enable_chain_of_thought(self, value: bool) -> None:
+        """Set chain of thought.
+
+        :param value: Whether or not chain of thought is enabled.
+        :type value: bool
+        """
+        self._enable_chain_of_thought = value
+
+    @property
+    def enable_chain_of_density(self) -> bool:
+        """Get whether or not chain of density is enabled.
+
+        :return: Whether or not chain of thought is enabled
+        :rtype: bool
+        """
+        return self._enable_chain_of_density
+
+    @enable_chain_of_density.setter
+    def enable_chain_of_density(self, value: bool) -> None:
+        """Set whether or not chain of thought is enabled.
+
+        :param value: Whether or not chain of thought is enabled
+        :type value: bool
+        """
+        self._enable_chain_of_density = value
+
+    @property
+    def max_len_summary(self) -> Optional[int]:
+        """The number of tokens to use for summarization.
+
+        :return: The number of tokens to use for summarization
+        :rtype: typing.Optional[int]
+        """
+        return self._max_len_summary
+
+    @max_len_summary.setter
+    def max_len_summary(self, length: Optional[int]) -> None:
+        """Set the number of tokens to use for summarization.
+
+        :param length: The number of tokens to use for summarization.
+        :type length: typing.Optional[int]
+        """
+        self._max_len_summary = length
+
+    # @property
+    # def custom_prompt(self) -> Optional[str]:
+    #     """Get the custom system prompt to use for inferencing.
+    #     :return: The custom prompt to use for inferencing.
+    #     :rtype: Optional[str]
+    #     """
+    #     return self._custom_prompt
+
+    # @custom_prompt.setter
+    # def custom_prompt(self, prompt: Optional[str]) -> None:
+    #     """Set the custom prompt to use for inferencing.
+
+    #     :param prompt: The custom prompt to use for inferencing.
+    #     :type prompt: Optional[str]
+    #     """
+    #     self._custom_prompt = prompt
+
+    def items(self):
+        return self.__dict__.items()
+
+    def __eq__(self, other: object) -> bool:
+        """Returns True if both instances have the same values.
+
+        This method check instances equality and returns True if both of
+            the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, PromptSettings):
+            return False
+        return (
+            self.enable_chain_of_thought == other.enable_chain_of_thought
+            and self.enable_chain_of_density == other.enable_chain_of_density
+            and self.max_len_summary == other.max_len_summary
+            # self.custom_prompt == other.custom_prompt
+        )
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two PromptSettings objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/teacher_model_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/teacher_model_settings.py
new file mode 100644
index 00000000..481800de
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distillation/teacher_model_settings.py
@@ -0,0 +1,93 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from typing import Dict, Optional
+
+from azure.ai.ml._utils._experimental import experimental
+from azure.ai.ml.entities._job.distillation.endpoint_request_settings import EndpointRequestSettings
+
+
+@experimental
+class TeacherModelSettings:
+    def __init__(
+        self,
+        *,
+        inference_parameters: Optional[Dict] = None,
+        endpoint_request_settings: Optional[EndpointRequestSettings] = None,
+    ):
+        """Initialize TeacherModelSettings
+
+        :param inference_parameters: The inference parameters inferencing requests will use, defaults to None
+        :type inference_parameters: typing.Optional[typing.Dict], optional
+        :param endpoint_request_settings: The settings to use for the endpoint, defaults to None
+        :type endpoint_request_settings: typing.Optional[EndpointRequestSettings], optional
+        """
+        self._inference_parameters = inference_parameters
+        self._endpoint_request_settings = endpoint_request_settings
+
+    @property
+    def inference_parameters(self) -> Optional[Dict]:
+        """Get the inference parameters.
+
+        :return: The inference parameters.
+        :rtype: typing.Optional[typing.Dict]
+        """
+        return self._inference_parameters
+
+    @inference_parameters.setter
+    def inference_parameters(self, params: Optional[Dict]) -> None:
+        """Set the inference parameters.
+
+        :param params: Inference parameters.
+        :type params: typing.Optional[typing.Dict]
+        """
+        self._inference_parameters = params
+
+    @property
+    def endpoint_request_settings(self) -> Optional[EndpointRequestSettings]:
+        """Get the endpoint request settings.
+
+        :return: The endpoint request settings.
+        :rtype: typing.Optional[EndpointRequestSettings]
+        """
+        return self._endpoint_request_settings
+
+    @endpoint_request_settings.setter
+    def endpoint_request_settings(self, endpoint_settings: Optional[EndpointRequestSettings]) -> None:
+        """Set the endpoint request settings.
+
+        :param endpoint_settings: Endpoint request settings
+        :type endpoint_settings: typing.Optional[EndpointRequestSettings]
+        """
+        self._endpoint_request_settings = endpoint_settings
+
+    def items(self):
+        return self.__dict__.items()
+
+    def __eq__(self, other: object) -> bool:
+        """Returns True if both instances have the same values.
+
+        This method check instances equality and returns True if both of
+            the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, TeacherModelSettings):
+            return False
+        return (
+            self.inference_parameters == other.inference_parameters
+            and self.endpoint_request_settings == other.endpoint_request_settings
+        )
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two TeacherModelSettings objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distribution.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distribution.py
new file mode 100644
index 00000000..ec7277c6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/distribution.py
@@ -0,0 +1,229 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=unused-argument
+
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    DistributionConfiguration as RestDistributionConfiguration,
+)
+from azure.ai.ml._restclient.v2023_04_01_preview.models import DistributionType as RestDistributionType
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Mpi as RestMpi
+from azure.ai.ml._restclient.v2023_04_01_preview.models import PyTorch as RestPyTorch
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Ray as RestRay
+from azure.ai.ml._restclient.v2023_04_01_preview.models import TensorFlow as RestTensorFlow
+from azure.ai.ml._utils._experimental import experimental
+from azure.ai.ml.constants import DistributionType
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+SDK_TO_REST = {
+    DistributionType.MPI: RestDistributionType.MPI,
+    DistributionType.TENSORFLOW: RestDistributionType.TENSOR_FLOW,
+    DistributionType.PYTORCH: RestDistributionType.PY_TORCH,
+    DistributionType.RAY: RestDistributionType.RAY,
+}
+
+
+class DistributionConfiguration(RestTranslatableMixin):
+    """Distribution configuration for a component or job.
+
+    This class is not meant to be instantiated directly. Instead, use one of its subclasses.
+    """
+
+    def __init__(self, **kwargs: Any) -> None:
+        self.type: Any = None
+
+    @classmethod
+    def _from_rest_object(
+        cls, obj: Optional[Union[RestDistributionConfiguration, Dict]]
+    ) -> Optional["DistributionConfiguration"]:
+        """Constructs a DistributionConfiguration object from a REST object
+
+        This function works for distribution property of a Job object and of a Component object()
+
+        Distribution of Job when returned by MFE, is a RestDistributionConfiguration
+
+        Distribution of Component when returned by MFE, is a Dict.
+        e.g. {'type': 'Mpi', 'process_count_per_instance': '1'}
+
+        So in the job distribution case, we need to call as_dict() first and get type from "distribution_type" property.
+        In the componenet case, we need to extract type from key "type"
+
+
+        :param obj: The object to translate
+        :type obj: Optional[Union[RestDistributionConfiguration, Dict]]
+        :return: The distribution configuration
+        :rtype: DistributionConfiguration
+        """
+        if obj is None:
+            return None
+
+        if isinstance(obj, dict):
+            data = obj
+        else:
+            data = obj.as_dict()
+
+        type_str = data.pop("distribution_type", None) or data.pop("type", None)
+        klass = DISTRIBUTION_TYPE_MAP[type_str.lower()]
+        res: DistributionConfiguration = klass(**data)
+        return res
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, DistributionConfiguration):
+            return NotImplemented
+        res: bool = self._to_rest_object() == other._to_rest_object()
+        return res
+
+
+class MpiDistribution(DistributionConfiguration):
+    """MPI distribution configuration.
+
+    :keyword process_count_per_instance: The number of processes per node.
+    :paramtype process_count_per_instance: Optional[int]
+    :ivar type: Specifies the type of distribution. Set automatically to "mpi" for this class.
+    :vartype type: str
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_misc.py
+            :start-after: [START mpi_distribution_configuration]
+            :end-before: [END mpi_distribution_configuration]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a CommandComponent with an MpiDistribution.
+    """
+
+    def __init__(self, *, process_count_per_instance: Optional[int] = None, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        self.type = DistributionType.MPI
+        self.process_count_per_instance = process_count_per_instance
+
+    def _to_rest_object(self) -> RestMpi:
+        return RestMpi(process_count_per_instance=self.process_count_per_instance)
+
+
+class PyTorchDistribution(DistributionConfiguration):
+    """PyTorch distribution configuration.
+
+    :keyword process_count_per_instance: The number of processes per node.
+    :paramtype process_count_per_instance: Optional[int]
+    :ivar type: Specifies the type of distribution. Set automatically to "pytorch" for this class.
+    :vartype type: str
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_misc.py
+            :start-after: [START pytorch_distribution_configuration]
+            :end-before: [END pytorch_distribution_configuration]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a CommandComponent with a PyTorchDistribution.
+    """
+
+    def __init__(self, *, process_count_per_instance: Optional[int] = None, **kwargs: Any) -> None:
+        super().__init__(**kwargs)
+        self.type = DistributionType.PYTORCH
+        self.process_count_per_instance = process_count_per_instance
+
+    def _to_rest_object(self) -> RestPyTorch:
+        return RestPyTorch(process_count_per_instance=self.process_count_per_instance)
+
+
+class TensorFlowDistribution(DistributionConfiguration):
+    """TensorFlow distribution configuration.
+
+    :vartype distribution_type: str or ~azure.mgmt.machinelearningservices.models.DistributionType
+    :keyword parameter_server_count: The number of parameter server tasks. Defaults to 0.
+    :paramtype parameter_server_count: Optional[int]
+    :keyword worker_count: The number of workers. Defaults to the instance count.
+    :paramtype worker_count: Optional[int]
+    :ivar parameter_server_count: Number of parameter server tasks.
+    :vartype parameter_server_count: int
+    :ivar worker_count: Number of workers. If not specified, will default to the instance count.
+    :vartype worker_count: int
+    :ivar type: Specifies the type of distribution. Set automatically to "tensorflow" for this class.
+    :vartype type: str
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_misc.py
+            :start-after: [START tensorflow_distribution_configuration]
+            :end-before: [END tensorflow_distribution_configuration]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a CommandComponent with a TensorFlowDistribution.
+    """
+
+    def __init__(
+        self, *, parameter_server_count: Optional[int] = 0, worker_count: Optional[int] = None, **kwargs: Any
+    ) -> None:
+        super().__init__(**kwargs)
+        self.type = DistributionType.TENSORFLOW
+        self.parameter_server_count = parameter_server_count
+        self.worker_count = worker_count
+
+    def _to_rest_object(self) -> RestTensorFlow:
+        return RestTensorFlow(parameter_server_count=self.parameter_server_count, worker_count=self.worker_count)
+
+
+@experimental
+class RayDistribution(DistributionConfiguration):
+    """Ray distribution configuration.
+
+    :vartype distribution_type: str or ~azure.mgmt.machinelearningservices.models.DistributionType
+    :ivar port: The port of the head ray process.
+    :vartype port: int
+    :ivar address: The address of Ray head node.
+    :vartype address: str
+    :ivar include_dashboard: Provide this argument to start the Ray dashboard GUI.
+    :vartype include_dashboard: bool
+    :ivar dashboard_port: The port to bind the dashboard server to.
+    :vartype dashboard_port: int
+    :ivar head_node_additional_args: Additional arguments passed to ray start in head node.
+    :vartype head_node_additional_args: str
+    :ivar worker_node_additional_args: Additional arguments passed to ray start in worker node.
+    :vartype worker_node_additional_args: str
+    :ivar type: Specifies the type of distribution. Set automatically to "Ray" for this class.
+    :vartype type: str
+    """
+
+    def __init__(
+        self,
+        *,
+        port: Optional[int] = None,
+        address: Optional[str] = None,
+        include_dashboard: Optional[bool] = None,
+        dashboard_port: Optional[int] = None,
+        head_node_additional_args: Optional[str] = None,
+        worker_node_additional_args: Optional[str] = None,
+        **kwargs: Any
+    ):
+        super().__init__(**kwargs)
+        self.type = DistributionType.RAY
+
+        self.port = port
+        self.address = address
+        self.include_dashboard = include_dashboard
+        self.dashboard_port = dashboard_port
+        self.head_node_additional_args = head_node_additional_args
+        self.worker_node_additional_args = worker_node_additional_args
+
+    def _to_rest_object(self) -> RestRay:
+        return RestRay(
+            port=self.port,
+            address=self.address,
+            include_dashboard=self.include_dashboard,
+            dashboard_port=self.dashboard_port,
+            head_node_additional_args=self.head_node_additional_args,
+            worker_node_additional_args=self.worker_node_additional_args,
+        )
+
+
+DISTRIBUTION_TYPE_MAP = {
+    DistributionType.MPI: MpiDistribution,
+    DistributionType.TENSORFLOW: TensorFlowDistribution,
+    DistributionType.PYTORCH: PyTorchDistribution,
+    DistributionType.RAY: RayDistribution,
+}
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/__init__.py
new file mode 100644
index 00000000..fdf8caba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/__init__.py
@@ -0,0 +1,5 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/azure_openai_finetuning_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/azure_openai_finetuning_job.py
new file mode 100644
index 00000000..e659c634
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/azure_openai_finetuning_job.py
@@ -0,0 +1,242 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict
+
+from azure.ai.ml._restclient.v2024_01_01_preview.models import (
+    ModelProvider as RestModelProvider,
+    AzureOpenAiFineTuning as RestAzureOpenAIFineTuning,
+    FineTuningJob as RestFineTuningJob,
+    JobBase as RestJobBase,
+)
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.entities._job._input_output_helpers import from_rest_data_outputs, to_rest_data_outputs
+
+from azure.ai.ml.entities._job.finetuning.finetuning_vertical import FineTuningVertical
+from azure.ai.ml.entities._job.finetuning.azure_openai_hyperparameters import AzureOpenAIHyperparameters
+from azure.ai.ml.entities._util import load_from_dict
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+from azure.ai.ml._utils._experimental import experimental
+
+
+@experimental
+class AzureOpenAIFineTuningJob(FineTuningVertical):
+    def __init__(
+        self,
+        **kwargs: Any,
+    ) -> None:
+        # Extract any task specific settings
+        model = kwargs.pop("model", None)
+        task = kwargs.pop("task", None)
+        # Convert task to lowercase first letter, this is when we create
+        # object from the schema, using dict object from the REST api response.
+        # TextCompletion => textCompletion
+        if task:
+            task = task[0].lower() + task[1:]
+        training_data = kwargs.pop("training_data", None)
+        validation_data = kwargs.pop("validation_data", None)
+        hyperparameters = kwargs.pop("hyperparameters", None)
+        if hyperparameters and not isinstance(hyperparameters, AzureOpenAIHyperparameters):
+            raise ValidationException(
+                category=ErrorCategory.USER_ERROR,
+                target=ErrorTarget.JOB,
+                message="Hyperparameters if provided should of type AzureOpenAIHyperparameters",
+                no_personal_data_message="Hyperparameters if provided should of type AzureOpenAIHyperparameters",
+            )
+
+        self._hyperparameters = hyperparameters
+
+        super().__init__(
+            task=task,
+            model=model,
+            model_provider=RestModelProvider.AZURE_OPEN_AI,
+            training_data=training_data,
+            validation_data=validation_data,
+            **kwargs,
+        )
+
+    @property
+    def hyperparameters(self) -> AzureOpenAIHyperparameters:
+        """Get hyperparameters.
+
+        :return: Hyperparameters for finetuning the model.
+        :rtype: AzureOpenAIHyperparameters
+        """
+        return self._hyperparameters
+
+    @hyperparameters.setter
+    def hyperparameters(self, hyperparameters: AzureOpenAIHyperparameters) -> None:
+        """Set hyperparameters.
+
+        :param hyperparameters: Hyperparameters for finetuning the model.
+        :type hyperparameters: AzureOpenAiHyperParameters
+        """
+        self._hyperparameters = hyperparameters
+
+    def _to_rest_object(self) -> "RestFineTuningJob":
+        """Convert CustomFineTuningVertical object to a RestFineTuningJob object.
+
+        :return: REST object representation of this object.
+        :rtype: JobBase
+        """
+        aoai_finetuning_vertical = RestAzureOpenAIFineTuning(
+            task_type=self._task,
+            model=self._model,
+            model_provider=self._model_provider,
+            training_data=self._training_data,
+            validation_data=self._validation_data,
+            hyper_parameters=self.hyperparameters._to_rest_object() if self.hyperparameters else None,
+        )
+
+        self._resolve_inputs(aoai_finetuning_vertical)
+
+        finetuning_job = RestFineTuningJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            tags=self.tags,
+            properties=self.properties,
+            fine_tuning_details=aoai_finetuning_vertical,
+            outputs=to_rest_data_outputs(self.outputs),
+        )
+
+        result = RestJobBase(properties=finetuning_job)
+        result.name = self.name
+
+        return result
+
+    def _to_dict(self) -> Dict:
+        """Convert the object to a dictionary.
+
+        :return: dictionary representation of the object.
+        :rtype: typing.Dict
+        """
+        from azure.ai.ml._schema._finetuning.azure_openai_finetuning import AzureOpenAIFineTuningSchema
+
+        schema_dict: dict = {}
+        # TODO: Combeback to this later for FineTuningJob in Pipelines
+        # if inside_pipeline:
+        #    schema_dict = AutoMLClassificationNodeSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        # else:
+        schema_dict = AzureOpenAIFineTuningSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+
+        return schema_dict
+
+    def __eq__(self, other: object) -> bool:
+        """Returns True if both instances have the same values.
+
+        This method check instances equality and returns True if both of
+            the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, AzureOpenAIFineTuningJob):
+            return NotImplemented
+
+        return super().__eq__(other) and self.hyperparameters == other.hyperparameters
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two AzureOpenAIFineTuningJob objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestJobBase) -> "AzureOpenAIFineTuningJob":
+        """Convert a REST object to AzureOpenAIFineTuningJob object.
+
+        :param obj: AzureOpenAIFineTuningJob in Rest format.
+        :type obj: JobBase
+        :return: AzureOpenAIFineTuningJob objects.
+        :rtype: AzureOpenAIFineTuningJob
+        """
+
+        properties: RestFineTuningJob = obj.properties
+        finetuning_details: RestAzureOpenAIFineTuning = properties.fine_tuning_details
+
+        job_args_dict = {
+            "id": obj.id,
+            "name": obj.name,
+            "description": properties.description,
+            "tags": properties.tags,
+            "properties": properties.properties,
+            "experiment_name": properties.experiment_name,
+            "status": properties.status,
+            "creation_context": obj.system_data,
+            "display_name": properties.display_name,
+            "outputs": from_rest_data_outputs(properties.outputs),
+        }
+
+        aoai_finetuning_job = cls(
+            task=finetuning_details.task_type,
+            model=finetuning_details.model,
+            training_data=finetuning_details.training_data,
+            validation_data=finetuning_details.validation_data,
+            hyperparameters=AzureOpenAIHyperparameters._from_rest_object(finetuning_details.hyper_parameters),
+            **job_args_dict,
+        )
+
+        aoai_finetuning_job._restore_inputs()
+
+        return aoai_finetuning_job
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "AzureOpenAIFineTuningJob":
+        """Load from a dictionary.
+
+        :param data: dictionary representation of the object.
+        :type data: typing.Dict
+        :param context: dictionary containing the context.
+        :type context: typing.Dict
+        :param additional_message: additional message to be added to the error message.
+        :type additional_message: str
+        :return: AzureOpenAIFineTuningJob object.
+        :rtype: AzureOpenAIFineTuningJob
+        """
+        from azure.ai.ml._schema._finetuning.azure_openai_finetuning import AzureOpenAIFineTuningSchema
+
+        # TODO: Combeback to this later - Pipeline part.
+        # from azure.ai.ml._schema.pipeline.automl_node import AutoMLClassificationNodeSchema
+
+        # if kwargs.pop("inside_pipeline", False):
+        #    loaded_data = load_from_dict(
+        #        AutoMLClassificationNodeSchema,
+        #        data,
+        #        context,
+        #        additional_message,
+        #        **kwargs,
+        #    )
+        # else:
+        loaded_data = load_from_dict(AzureOpenAIFineTuningSchema, data, context, additional_message, **kwargs)
+
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "AzureOpenAIFineTuningJob":
+        """Create an instance from a schema dictionary.
+
+        :param loaded_data: dictionary containing the data.
+        :type loaded_data: typing.Dict
+        :return: AzureOpenAIFineTuningJob object.
+        :rtype: AzureOpenAIFineTuningJob
+        """
+
+        job = AzureOpenAIFineTuningJob(**loaded_data)
+        return job
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/azure_openai_hyperparameters.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/azure_openai_hyperparameters.py
new file mode 100644
index 00000000..2b420a46
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/azure_openai_hyperparameters.py
@@ -0,0 +1,125 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+from azure.ai.ml._restclient.v2024_01_01_preview.models import (
+    AzureOpenAiHyperParameters as RestAzureOpenAiHyperParameters,
+)
+from azure.ai.ml._utils._experimental import experimental
+
+
+@experimental
+class AzureOpenAIHyperparameters(RestTranslatableMixin):
+    """Hyperparameters for Azure OpenAI model finetuning."""
+
+    def __init__(
+        self,
+        *,
+        batch_size: Optional[int] = None,
+        learning_rate_multiplier: Optional[float] = None,
+        n_epochs: Optional[int] = None,
+    ):
+        """Initialize AzureOpenAIHyperparameters.
+
+        param batch_size: Number of examples in each batch.
+                          A larger batch size means that model parameters are updated less
+                          frequently, but with lower variance. Defaults to None.
+        type batch_size: int
+        param learning_rate_multiplier: Scaling factor for the learning rate.
+                                        A smaller learning rate may be useful to avoid overfitting.
+        type learning_rate_multiplier: float
+        param n_epochs: The number of epochs to train the model for.
+                        An epoch refers to one full cycle through the training dataset.
+        type n_epochs: int
+        """
+        self._batch_size = batch_size
+        self._learning_rate_multiplier = learning_rate_multiplier
+        self._n_epochs = n_epochs
+        # Not exposed in the public API, so need to check how to handle this
+        # self._additional_properties = kwargs
+
+    @property
+    def batch_size(self) -> Optional[int]:
+        """Get the batch size for training."""
+        return self._batch_size
+
+    @batch_size.setter
+    def batch_size(self, value: Optional[int]) -> None:
+        """Set the batch size for training.
+        :param value: The batch size for training.
+        :type value: int
+        """
+        self._batch_size = value
+
+    @property
+    def learning_rate_multiplier(self) -> Optional[float]:
+        """Get the learning rate multiplier.
+        :return: The learning rate multiplier.
+        :rtype: float
+        """
+        return self._learning_rate_multiplier
+
+    @learning_rate_multiplier.setter
+    def learning_rate_multiplier(self, value: Optional[float]) -> None:
+        """Set the learning rate multiplier.
+        :param value: The learning rate multiplier.
+        :type value: float
+        """
+        self._learning_rate_multiplier = value
+
+    @property
+    def n_epochs(self) -> Optional[int]:
+        """Get the number of epochs.
+        :return: The number of epochs.
+        :rtype: int
+        """
+        return self._n_epochs
+
+    @n_epochs.setter
+    def n_epochs(self, value: Optional[int]) -> None:
+        """Set the number of epochs.
+        :param value: The number of epochs.
+        :type value: int
+        """
+        self._n_epochs = value
+
+    # Not exposed in the public API, so need to check how to handle this
+    # @property
+    # def additional_properties(self) -> dict:
+    #    """Get additional properties."""
+    #    return self._additional_properties
+
+    # @additional_properties.setter
+    # def additional_properties(self, value: dict) -> None:
+    #    """Set additional properties."""
+    #    self._additional_properties = value
+
+    def _to_rest_object(self) -> RestAzureOpenAiHyperParameters:
+        return RestAzureOpenAiHyperParameters(
+            batch_size=self._batch_size,
+            learning_rate_multiplier=self._learning_rate_multiplier,
+            n_epochs=self._n_epochs,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, AzureOpenAIHyperparameters):
+            return NotImplemented
+        return (
+            self._batch_size == other._batch_size
+            and self._learning_rate_multiplier == other._learning_rate_multiplier
+            and self._n_epochs == other._n_epochs
+        )
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestAzureOpenAiHyperParameters) -> "AzureOpenAIHyperparameters":
+        aoai_hyperparameters = cls(
+            batch_size=obj.batch_size,
+            learning_rate_multiplier=obj.learning_rate_multiplier,
+            n_epochs=obj.n_epochs,
+        )
+        return aoai_hyperparameters
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/custom_model_finetuning_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/custom_model_finetuning_job.py
new file mode 100644
index 00000000..e6ddd86d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/custom_model_finetuning_job.py
@@ -0,0 +1,258 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict
+
+from azure.ai.ml._restclient.v2024_10_01_preview.models import (
+    ModelProvider as RestModelProvider,
+    CustomModelFineTuning as RestCustomModelFineTuningVertical,
+    FineTuningJob as RestFineTuningJob,
+    JobBase as RestJobBase,
+)
+from azure.ai.ml.entities._job._input_output_helpers import (
+    from_rest_data_outputs,
+    to_rest_data_outputs,
+)
+from azure.ai.ml.entities._job.job_resources import JobResources
+from azure.ai.ml.entities._job.queue_settings import QueueSettings
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.entities._job.finetuning.finetuning_vertical import FineTuningVertical
+from azure.ai.ml.entities._util import load_from_dict
+from azure.ai.ml._utils._experimental import experimental
+
+
+@experimental
+class CustomModelFineTuningJob(FineTuningVertical):
+    def __init__(
+        self,
+        **kwargs: Any,
+    ) -> None:
+        # Extract any task specific settings
+        model = kwargs.pop("model", None)
+        task = kwargs.pop("task", None)
+        # Convert task to lowercase first letter, this is when we create
+        # object from the schema, using dict object from the REST api response.
+        # TextCompletion => textCompletion
+        if task:
+            task = task[0].lower() + task[1:]
+        training_data = kwargs.pop("training_data", None)
+        validation_data = kwargs.pop("validation_data", None)
+        self._hyperparameters = kwargs.pop("hyperparameters", None)
+        super().__init__(
+            task=task,
+            model=model,
+            model_provider=RestModelProvider.CUSTOM,
+            training_data=training_data,
+            validation_data=validation_data,
+            **kwargs,
+        )
+
+    @property
+    def hyperparameters(self) -> Dict[str, str]:
+        """Get hyperparameters.
+
+        :return:
+        :rtype: hyperparameters: Dict[str,str]
+        """
+        return self._hyperparameters
+
+    @hyperparameters.setter
+    def hyperparameters(self, hyperparameters: Dict[str, str]) -> None:
+        """Set hyperparameters.
+
+        :param hyperparameters: Hyperparameters for finetuning the model
+        :type hyperparameters: Dict[str,str]
+        """
+        self._hyperparameters = hyperparameters
+
+    def _to_rest_object(self) -> "RestFineTuningJob":
+        """Convert CustomFineTuningVertical object to a RestFineTuningJob object.
+
+        :return: REST object representation of this object.
+        :rtype: JobBase
+        """
+        custom_finetuning_vertical = RestCustomModelFineTuningVertical(
+            task_type=self._task,
+            model=self._model,
+            model_provider=self._model_provider,
+            training_data=self._training_data,
+            validation_data=self._validation_data,
+            hyper_parameters=self._hyperparameters,
+        )
+        self._resolve_inputs(custom_finetuning_vertical)
+
+        finetuning_job = RestFineTuningJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            services=self.services,
+            tags=self.tags,
+            properties=self.properties,
+            compute_id=self.compute,
+            fine_tuning_details=custom_finetuning_vertical,
+            outputs=to_rest_data_outputs(self.outputs),
+        )
+        if self.resources:
+            finetuning_job.resources = self.resources._to_rest_object()
+        if self.queue_settings:
+            finetuning_job.queue_settings = self.queue_settings._to_rest_object()
+
+        result = RestJobBase(properties=finetuning_job)
+        result.name = self.name
+
+        return result
+
+    def _to_dict(self) -> Dict:
+        """Convert the object to a dictionary.
+
+        :return: dictionary representation of the object.
+        :rtype: typing.Dict
+        """
+        from azure.ai.ml._schema._finetuning.custom_model_finetuning import (
+            CustomModelFineTuningSchema,
+        )
+
+        schema_dict: dict = {}
+        # TODO: Combeback to this later for FineTuningJob in pipeline
+        # if inside_pipeline:
+        #    schema_dict = AutoMLClassificationNodeSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        # else:
+        schema_dict = CustomModelFineTuningSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+
+        return schema_dict
+
+    def __eq__(self, other: object) -> bool:
+        """Returns True if both instances have the same values.
+
+        This method check instances equality and returns True if both of
+            the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, CustomModelFineTuningJob):
+            return NotImplemented
+
+        return super().__eq__(other) and self.hyperparameters == other.hyperparameters
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two CustomModelFineTuningJob objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestJobBase) -> "CustomModelFineTuningJob":
+        """Convert a REST object to CustomModelFineTuningJob object.
+
+        :param obj: CustomModelFineTuningJob in Rest format.
+        :type obj: JobBase
+        :return: CustomModelFineTuningJob objects.
+        :rtype: CustomModelFineTuningJob
+        """
+
+        properties: RestFineTuningJob = obj.properties
+        finetuning_details: RestCustomModelFineTuningVertical = properties.fine_tuning_details
+
+        job_args_dict = {
+            "id": obj.id,
+            "name": obj.name,
+            "description": properties.description,
+            "tags": properties.tags,
+            "properties": properties.properties,
+            "services": properties.services,
+            "experiment_name": properties.experiment_name,
+            "status": properties.status,
+            "creation_context": obj.system_data,
+            "display_name": properties.display_name,
+            "compute": properties.compute_id,
+            "outputs": from_rest_data_outputs(properties.outputs),
+        }
+
+        if properties.resources:
+            job_args_dict["resources"] = JobResources._from_rest_object(properties.resources)
+        if properties.queue_settings:
+            job_args_dict["queue_settings"] = QueueSettings._from_rest_object(properties.queue_settings)
+
+        custom_model_finetuning_job = cls(
+            task=finetuning_details.task_type,
+            model=finetuning_details.model,
+            training_data=finetuning_details.training_data,
+            validation_data=finetuning_details.validation_data,
+            hyperparameters=finetuning_details.hyper_parameters,
+            **job_args_dict,
+        )
+
+        custom_model_finetuning_job._restore_inputs()
+
+        return custom_model_finetuning_job
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "CustomModelFineTuningJob":
+        """Load from a dictionary.
+
+        :param data: dictionary representation of the object.
+        :type data: typing.Dict
+        :param context: dictionary containing the context.
+        :type context: typing.Dict
+        :param additional_message: additional message to be added to the error message.
+        :type additional_message: str
+        :return: CustomModelFineTuningJob object.
+        :rtype: CustomModelFineTuningJob
+        """
+        from azure.ai.ml._schema._finetuning.custom_model_finetuning import (
+            CustomModelFineTuningSchema,
+        )
+
+        # TODO: Combeback to this later - Pipeline part.
+        # from azure.ai.ml._schema.pipeline.automl_node import AutoMLClassificationNodeSchema
+
+        # if kwargs.pop("inside_pipeline", False):
+        #    loaded_data = load_from_dict(
+        #        AutoMLClassificationNodeSchema,
+        #        data,
+        #        context,
+        #        additional_message,
+        #        **kwargs,
+        #    )
+        # else:
+        loaded_data = load_from_dict(CustomModelFineTuningSchema, data, context, additional_message, **kwargs)
+
+        training_data = loaded_data.get("training_data", None)
+        if isinstance(training_data, str):
+            loaded_data["training_data"] = Input(type="uri_file", path=training_data)
+
+        validation_data = loaded_data.get("validation_data", None)
+        if isinstance(validation_data, str):
+            loaded_data["validation_data"] = Input(type="uri_file", path=validation_data)
+
+        job_instance = cls._create_instance_from_schema_dict(loaded_data)
+        return job_instance
+
+    @classmethod
+    def _create_instance_from_schema_dict(cls, loaded_data: Dict) -> "CustomModelFineTuningJob":
+        """Create an instance from a schema dictionary.
+
+        :param loaded_data: dictionary containing the data.
+        :type loaded_data: typing.Dict
+        :return: CustomModelFineTuningJob object.
+        :rtype: CustomModelFineTuningJob
+        """
+        job = CustomModelFineTuningJob(**loaded_data)
+        return job
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/finetuning_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/finetuning_job.py
new file mode 100644
index 00000000..ec8d9d5d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/finetuning_job.py
@@ -0,0 +1,224 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from typing import Any, Dict, Optional
+
+from azure.ai.ml.entities._job.job import Job
+from azure.ai.ml.entities._job.job_io_mixin import JobIOMixin
+from azure.ai.ml._restclient.v2024_10_01_preview.models import (
+    ModelProvider as RestModelProvider,
+    JobBase as RestJobBase,
+)
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.constants._common import TYPE
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.entities._job.job_resources import JobResources
+from azure.ai.ml.entities._job.queue_settings import QueueSettings
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+from azure.ai.ml.constants._job.finetuning import FineTuningConstants
+from azure.ai.ml._utils._experimental import experimental
+
+
+@experimental
+class FineTuningJob(Job, JobIOMixin):
+    def __init__(
+        self,
+        **kwargs: Any,
+    ) -> None:
+        kwargs[TYPE] = JobType.FINE_TUNING
+        self.resources = kwargs.pop("resources", None)
+        self.queue_settings = kwargs.pop("queue_settings", None)
+        self.outputs = kwargs.pop("outputs", None)
+        super().__init__(**kwargs)
+
+    @property
+    def resources(self) -> Optional[JobResources]:
+        """Job resources to use during job execution.
+        :return: Job Resources object.
+        :rtype: JobResources
+        """
+        return self._resources if hasattr(self, "_resources") else None
+
+    @resources.setter
+    def resources(self, value: JobResources) -> None:
+        """Set JobResources.
+
+        :param value: JobResources object.
+        :type value: JobResources
+        :raises ValidationException: Expected a JobResources object.
+        """
+        if isinstance(value, JobResources):
+            self._resources = value
+        elif value:
+            msg = "Expected an instance of JobResources."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.FINETUNING,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    @property
+    def queue_settings(self) -> Optional[QueueSettings]:
+        """Queue settings for job execution.
+        :return: QueueSettings object.
+        :rtype: QueueSettings
+        """
+        return self._queue_settings if hasattr(self, "_queue_settings") else None
+
+    @queue_settings.setter
+    def queue_settings(self, value: QueueSettings) -> None:
+        """Set queue settings for job execution.
+
+        :param value: QueueSettings object.
+        :type value: QueueSettings
+        :raises ValidationException: Expected a QueueSettings object.
+        """
+        if isinstance(value, QueueSettings):
+            self._queue_settings = value
+        elif value:
+            msg = "Expected an instance of QueueSettings."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.FINETUNING,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    def __eq__(self, other: object) -> bool:
+        """Returns True if both instances have the same values.
+
+        This method check instances equality and returns True if both of
+            the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, FineTuningJob):
+            return NotImplemented
+
+        queue_settings_match = (not self.queue_settings and not other.queue_settings) or (
+            self.queue_settings is not None
+            and other.queue_settings is not None
+            and self.queue_settings.job_tier is not None
+            and other.queue_settings.job_tier is not None
+            and self.queue_settings.job_tier.lower() == other.queue_settings.job_tier.lower()
+        )
+
+        outputs_match = not self.outputs and not other.outputs
+        if self.outputs and other.outputs:
+            outputs_match = (
+                self.outputs["registered_model"].name == other.outputs["registered_model"].name
+                and self.outputs["registered_model"].type == other.outputs["registered_model"].type
+            )
+
+        return (
+            outputs_match
+            and self.resources == other.resources
+            and queue_settings_match
+            # add properties from base class
+            and self.name == other.name
+            and self.description == other.description
+            and self.tags == other.tags
+            and self.properties == other.properties
+            and self.compute == other.compute
+            and self.id == other.id
+            and self.experiment_name == other.experiment_name
+            and self.status == other.status
+        )
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two FineTuningJob objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
+
+    @classmethod
+    def _get_model_provider_mapping(cls) -> Dict:
+        """Create a mapping of task type to job class.
+
+        :return: An FineTuningVertical object containing the model provider type to job class mapping.
+        :rtype: FineTuningJob
+        """
+        from .custom_model_finetuning_job import CustomModelFineTuningJob
+        from .azure_openai_finetuning_job import AzureOpenAIFineTuningJob
+
+        return {
+            camel_to_snake(RestModelProvider.CUSTOM): CustomModelFineTuningJob,
+            camel_to_snake(RestModelProvider.AZURE_OPEN_AI): AzureOpenAIFineTuningJob,
+        }
+
+    @classmethod
+    def _load_from_rest(cls, obj: RestJobBase) -> "FineTuningJob":
+        """Loads the rest object to a dict containing items to init the AutoMLJob objects.
+
+        :param obj: Azure Resource Manager resource envelope.
+        :type obj: JobBase
+        :raises ValidationException: task type validation error
+        :return: A FineTuningJob
+        :rtype: FineTuningJob
+        """
+        model_provider = (
+            camel_to_snake(obj.properties.fine_tuning_details.model_provider)
+            if obj.properties.fine_tuning_details.model_provider
+            else None
+        )
+        class_type = cls._get_model_provider_mapping().get(model_provider, None)
+        if class_type:
+            res: FineTuningJob = class_type._from_rest_object(obj)
+            return res
+        msg = f"Unsupported model provider type: {obj.properties.fine_tuning_details.model_provider}"
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.FINETUNING,
+            error_category=ErrorCategory.SYSTEM_ERROR,
+        )
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: Dict,
+        context: Dict,
+        additional_message: str,
+        **kwargs: Any,
+    ) -> "FineTuningJob":
+        """Loads the dictionary objects to an FineTuningJob object.
+
+        :param data: A data dictionary.
+        :type data: typing.Dict
+        :param context: A context dictionary.
+        :type context: typing.Dict
+        :param additional_message: An additional message to be logged in the ValidationException.
+        :type additional_message: str
+
+        :raises ValidationException: task type validation error
+        :return: An FineTuningJob
+        :rtype: FineTuningJob
+        """
+        model_provider = data.get(FineTuningConstants.ModelProvider)
+        class_type = cls._get_model_provider_mapping().get(model_provider, None)
+        if class_type:
+            res: FineTuningJob = class_type._load_from_dict(
+                data,
+                context,
+                additional_message,
+                **kwargs,
+            )
+            return res
+        msg = f"Unsupported model provider type: {model_provider}"
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.AUTOML,
+            error_category=ErrorCategory.USER_ERROR,
+        )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/finetuning_vertical.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/finetuning_vertical.py
new file mode 100644
index 00000000..c9a5fe41
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/finetuning/finetuning_vertical.py
@@ -0,0 +1,202 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Any, Optional, cast
+
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+from azure.ai.ml._restclient.v2024_10_01_preview.models import (
+    ModelProvider as RestModelProvider,
+    FineTuningVertical as RestFineTuningVertical,
+    UriFileJobInput,
+    MLFlowModelJobInput,
+)
+from azure.ai.ml.constants._common import AssetTypes
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job.finetuning.finetuning_job import FineTuningJob
+
+from azure.ai.ml._utils._experimental import experimental
+
+
+@experimental
+class FineTuningVertical(FineTuningJob):
+    def __init__(
+        self,
+        *,
+        task: str,
+        model: Input,
+        model_provider: Optional[str],
+        training_data: Input,
+        validation_data: Optional[Input] = None,
+        **kwargs: Any,
+    ) -> None:
+        self._task = task
+        self._model = model
+        self._model_provider = model_provider
+        self._training_data = training_data
+        self._validation_data = validation_data
+        super().__init__(**kwargs)
+
+    @property
+    def task(self) -> str:
+        """Get finetuning task.
+
+        :return: The type of task to run. Possible values include: "ChatCompletion"
+                 "TextCompletion", "TextClassification", "QuestionAnswering","TextSummarization",
+                 "TokenClassification", "TextTranslation", "ImageClassification", "ImageInstanceSegmentation",
+                 "ImageObjectDetection","VideoMultiObjectTracking".
+
+        :rtype: str
+        """
+        return self._task
+
+    @task.setter
+    def task(self, task: str) -> None:
+        """Set finetuning task.
+
+        :param task: The type of task to run. Possible values include: "ChatCompletion"
+                 "TextCompletion", "TextClassification", "QuestionAnswering","TextSummarization",
+                 "TokenClassification", "TextTranslation", "ImageClassification", "ImageInstanceSegmentation",
+                 "ImageObjectDetection","VideoMultiObjectTracking",.
+        :type task: str
+
+        :return: None
+        """
+        self._task = task
+
+    @property
+    def model(self) -> Optional[Input]:
+        """The model to be fine-tuned.
+        :return: Input object representing the mlflow model to be fine-tuned.
+        :rtype: Input
+        """
+        return self._model
+
+    @model.setter
+    def model(self, value: Input) -> None:
+        """Set the model to be fine-tuned.
+
+        :param value: Input object representing the mlflow model to be fine-tuned.
+        :type value: Input
+        :raises ValidationException: Expected a mlflow model input.
+        """
+        if isinstance(value, Input) and (cast(Input, value).type in ("mlflow_model", "custom_model")):
+            self._model = value
+        else:
+            msg = "Expected a mlflow model input or custom model input."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.FINETUNING,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    @property
+    def model_provider(self) -> Optional[str]:
+        """The model provider.
+        :return: The model provider.
+        :rtype: str
+        """
+        return self._model_provider
+
+    @model_provider.setter
+    def model_provider(self, value: str) -> None:
+        """Set the model provider.
+
+        :param value: The model provider.
+        :type value: str
+        """
+        self._model_provider = RestModelProvider[camel_to_snake(value).upper()] if value else None
+
+    @property
+    def training_data(self) -> Input:
+        """Get training data.
+
+        :return: Training data input
+        :rtype: Input
+        """
+        return self._training_data
+
+    @training_data.setter
+    def training_data(self, training_data: Input) -> None:
+        """Set training data.
+
+        :param training_data: Training data input
+        :type training_data: Input
+        """
+        self._training_data = training_data
+
+    @property
+    def validation_data(self) -> Optional[Input]:
+        """Get validation data.
+
+        :return: Validation data input
+        :rtype: Input
+        """
+        return self._validation_data
+
+    @validation_data.setter
+    def validation_data(self, validation_data: Input) -> None:
+        """Set validation data.
+
+        :param validation_data: Validation data input
+        :type validation_data: Input
+        """
+        self._validation_data = validation_data
+
+    def _resolve_inputs(self, rest_job: RestFineTuningVertical) -> None:
+        """Resolve JobInputs to UriFileJobInput within data_settings.
+
+        :param rest_job: The rest job object.
+        :type rest_job: RestFineTuningVertical
+        """
+        if isinstance(rest_job.training_data, Input):
+            rest_job.training_data = UriFileJobInput(uri=rest_job.training_data.path)
+        if isinstance(rest_job.validation_data, Input):
+            rest_job.validation_data = UriFileJobInput(uri=rest_job.validation_data.path)
+        if isinstance(rest_job.model, Input):
+            rest_job.model = MLFlowModelJobInput(uri=rest_job.model.path)
+
+    def _restore_inputs(self) -> None:
+        """Restore UriFileJobInputs to JobInputs within data_settings."""
+        if isinstance(self.training_data, UriFileJobInput):
+            self.training_data = Input(type=AssetTypes.URI_FILE, path=self.training_data.uri)
+        if isinstance(self.validation_data, UriFileJobInput):
+            self.validation_data = Input(type=AssetTypes.URI_FILE, path=self.validation_data.uri)
+        if isinstance(self.model, MLFlowModelJobInput):
+            self.model = Input(type=AssetTypes.MLFLOW_MODEL, path=self.model.uri)
+
+    def __eq__(self, other: object) -> bool:
+        """Returns True if both instances have the same values.
+
+        This method check instances equality and returns True if both of
+            the instances have the same attributes with the same values.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        if not isinstance(other, FineTuningVertical):
+            return NotImplemented
+
+        return (
+            # TODO: Equality from base class does not work, no current precedence for this
+            super().__eq__(other)
+            and self.task == other.task
+            and self.model == other.model
+            and self.model_provider == other.model_provider
+            and self.training_data == other.training_data
+            and self.validation_data == other.validation_data
+        )
+
+    def __ne__(self, other: object) -> bool:
+        """Check inequality between two FineTuningJob objects.
+
+        :param other: Any object
+        :type other: object
+        :return: True or False
+        :rtype: bool
+        """
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/import_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/import_job.py
new file mode 100644
index 00000000..24d4ec90
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/import_job.py
@@ -0,0 +1,285 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import logging
+from abc import ABC, abstractmethod
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Dict, Optional
+
+from azure.ai.ml._restclient.v2022_02_01_preview.models import CommandJob as RestCommandJob
+from azure.ai.ml._restclient.v2022_02_01_preview.models import JobBaseData
+from azure.ai.ml._schema.job.import_job import ImportJobSchema
+from azure.ai.ml._utils.utils import is_private_preview_enabled
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE
+from azure.ai.ml.entities._inputs_outputs import Output
+from azure.ai.ml.entities._job._input_output_helpers import (
+    from_rest_data_outputs,
+    from_rest_inputs_to_dataset_literal,
+    to_rest_data_outputs,
+    to_rest_dataset_literal_inputs,
+)
+from azure.ai.ml.entities._job.job_io_mixin import JobIOMixin
+from azure.ai.ml.entities._util import load_from_dict
+from azure.ai.ml.exceptions import MlException
+
+from .job import Job
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._builders import Import
+    from azure.ai.ml.entities._component.import_component import ImportComponent
+
+module_logger = logging.getLogger(__name__)
+
+
+class ImportSource(ABC):
+    def __init__(
+        self,
+        *,
+        type: Optional[str] = None,  # pylint: disable=redefined-builtin
+        connection: Optional[str] = None,
+    ):
+        self.type = type
+        self.connection = connection
+
+    @abstractmethod
+    def _to_job_inputs(self) -> Dict[str, Optional[str]]:
+        pass
+
+    @classmethod
+    def _from_job_inputs(cls, job_inputs: Dict[str, str]) -> "ImportSource":
+        """Translate job inputs to import source.
+
+        :param job_inputs: The job inputs
+        :type job_inputs: Dict[str, str]
+        :return: The import source
+        :rtype: ImportSource
+        """
+        type = job_inputs.get("type")  # pylint: disable=redefined-builtin
+        connection = job_inputs.get("connection")
+        query = job_inputs.get("query")
+        path = job_inputs.get("path")
+
+        import_source = (
+            DatabaseImportSource(type=type, connection=connection, query=query)
+            if query is not None
+            else FileImportSource(type=type, connection=connection, path=path)
+        )
+        return import_source
+
+
+class DatabaseImportSource(ImportSource):
+    def __init__(
+        self,
+        *,
+        type: Optional[str] = None,  # pylint: disable=redefined-builtin
+        connection: Optional[str] = None,
+        query: Optional[str] = None,
+    ):
+        ImportSource.__init__(
+            self,
+            type=type,
+            connection=connection,
+        )
+        self.query = query
+
+    def _to_job_inputs(self) -> Dict[str, Optional[str]]:
+        """Translate source to command Inputs.
+
+        :return: The job inputs dict
+        :rtype: Dict[str, str]
+        """
+        inputs = {
+            "type": self.type,
+            "connection": self.connection,
+            "query": self.query,
+        }
+        return inputs
+
+
+class FileImportSource(ImportSource):
+    def __init__(
+        self,
+        *,
+        type: Optional[str] = None,  # pylint: disable=redefined-builtin
+        connection: Optional[str] = None,
+        path: Optional[str] = None,
+    ):
+        ImportSource.__init__(
+            self,
+            type=type,
+            connection=connection,
+        )
+        self.path = path
+
+    def _to_job_inputs(self) -> Dict[str, Optional[str]]:
+        """Translate source to command Inputs.
+
+        :return: The job inputs dict
+        :rtype: Dict[str, str]
+        """
+        inputs = {
+            "type": self.type,
+            "connection": self.connection,
+            "path": self.path,
+        }
+        return inputs
+
+
+class ImportJob(Job, JobIOMixin):
+    """Import job.
+
+    :param name: Name of the job.
+    :type name: str
+    :param description: Description of the job.
+    :type description: str
+    :param display_name: Display name of the job.
+    :type display_name: str
+    :param experiment_name:  Name of the experiment the job will be created under.
+        If None is provided, default will be set to current directory name.
+    :type experiment_name: str
+    :param source: Input source parameters to the import job.
+    :type source: azure.ai.ml.entities.DatabaseImportSource or FileImportSource
+    :param output: output data binding used in the job.
+    :type output: azure.ai.ml.Output
+    :param kwargs: A dictionary of additional configuration parameters.
+    :type kwargs: dict
+    """
+
+    def __init__(
+        self,
+        *,
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        display_name: Optional[str] = None,
+        experiment_name: Optional[str] = None,
+        source: Optional[ImportSource] = None,
+        output: Optional[Output] = None,
+        **kwargs: Any,
+    ):
+        kwargs[TYPE] = JobType.IMPORT
+
+        Job.__init__(
+            self,
+            name=name,
+            display_name=display_name,
+            description=description,
+            experiment_name=experiment_name,
+            **kwargs,
+        )
+
+        self.source = source
+        self.output = output
+
+    def _to_dict(self) -> Dict:
+        res: dict = ImportJobSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def _to_rest_object(self) -> JobBaseData:
+        # TODO: Remove in PuP
+        if not is_private_preview_enabled():
+            msg = JobType.IMPORT + " job not supported."
+            raise MlException(message=msg, no_personal_data_message=msg)
+
+        _inputs = self.source._to_job_inputs() if self.source is not None else None  # pylint: disable=protected-access
+        if self.compute is None:
+            msg = "compute cannot be None."
+            raise MlException(message=msg, no_personal_data_message=msg)
+
+        properties = RestCommandJob(
+            display_name=self.display_name,
+            description=self.description,
+            compute_id=self.compute,
+            experiment_name=self.experiment_name,
+            inputs=to_rest_dataset_literal_inputs(_inputs, job_type=self.type),
+            outputs=to_rest_data_outputs({"output": self.output}),
+            # TODO: Remove in PuP with native import job/component type support in MFE/Designer
+            # No longer applicable once new import job type is ready on MFE in PuP
+            # command and environment are required as we use command type for import
+            # command can be random string and the particular environment name here is defined as default in MFE
+            #   public const string DefaultEnvironmentName = "AzureML-sklearn-0.24-ubuntu18.04-py37-cpu";
+            # which is considered valid environment in MFE unless MFE changes current default logic
+            # but chance should be very low in PrP
+            command="import",
+            environment_id=self.compute.replace(
+                "/computes/DataFactory", "/environments/AzureML-sklearn-0.24-ubuntu18.04-py37-cpu"
+            ),
+        )
+        result = JobBaseData(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "ImportJob":
+        loaded_data = load_from_dict(ImportJobSchema, data, context, additional_message, **kwargs)
+        return ImportJob(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
+
+    @classmethod
+    def _load_from_rest(cls, obj: JobBaseData) -> "ImportJob":
+        rest_command_job: RestCommandJob = obj.properties
+        outputs = from_rest_data_outputs(rest_command_job.outputs)
+        inputs = from_rest_inputs_to_dataset_literal(rest_command_job.inputs)
+
+        import_job = ImportJob(
+            name=obj.name,
+            id=obj.id,
+            display_name=rest_command_job.display_name,
+            description=rest_command_job.description,
+            experiment_name=rest_command_job.experiment_name,
+            status=rest_command_job.status,
+            creation_context=obj.system_data,
+            source=ImportSource._from_job_inputs(inputs),  # pylint: disable=protected-access
+            output=outputs["output"] if "output" in outputs else None,
+        )
+        return import_job
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "ImportComponent":
+        """Translate a import job to component.
+
+        :param context: Context of import job YAML file.
+        :type context: dict
+        :return: Translated import component.
+        :rtype: ImportComponent
+        """
+        from azure.ai.ml.entities._component.import_component import ImportComponent
+
+        pipeline_job_dict = kwargs.get("pipeline_job_dict", {})
+        context = context or {BASE_PATH_CONTEXT_KEY: Path("import/")}
+
+        _inputs = self.source._to_job_inputs() if self.source is not None else None  # pylint: disable=protected-access
+
+        # Create anonymous command component with default version as 1
+        return ImportComponent(
+            is_anonymous=True,
+            base_path=context[BASE_PATH_CONTEXT_KEY],
+            description=self.description,
+            source=self._to_inputs(
+                inputs=_inputs,
+                pipeline_job_dict=pipeline_job_dict,
+            ),
+            output=self._to_outputs(outputs={"output": self.output}, pipeline_job_dict=pipeline_job_dict)["output"],
+        )
+
+    def _to_node(self, context: Optional[Dict] = None, **kwargs: Any) -> "Import":
+        """Translate a import job to a pipeline node.
+
+        :param context: Context of import job YAML file.
+        :type context: dict
+        :return: Translated import node.
+        :rtype: Import
+        """
+        from azure.ai.ml.entities._builders import Import
+
+        component = self._to_component(context, **kwargs)
+        _inputs = self.source._to_job_inputs() if self.source is not None else None  # pylint: disable=protected-access
+        return Import(
+            component=component,
+            compute=self.compute,
+            inputs=_inputs,
+            outputs={"output": self.output},
+            description=self.description,
+            display_name=self.display_name,
+            properties=self.properties,
+        )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/input_output_entry.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/input_output_entry.py
new file mode 100644
index 00000000..aa0e73b1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/input_output_entry.py
@@ -0,0 +1,27 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import collections.abc
+import logging
+from typing import Any, Optional, Union
+
+from azure.ai.ml.constants import InputOutputModes
+from azure.ai.ml.entities._assets import Data
+from azure.ai.ml.entities._mixins import DictMixin
+
+module_logger = logging.getLogger(__name__)
+
+
+class InputOutputEntry(DictMixin):
+    def __init__(
+        self,  # pylint: disable=unused-argument
+        data: Optional[Union[str, "Data"]] = None,
+        mode: Optional[str] = InputOutputModes.MOUNT,
+        **kwargs: Any,
+    ):
+        # Data will be either a dataset id, inline dataset definition
+        self.data = data
+        self.mode = mode
+        if isinstance(self.data, collections.abc.Mapping) and not isinstance(self.data, Data):
+            self.data = Data(**self.data)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/input_port.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/input_port.py
new file mode 100644
index 00000000..7953bbde
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/input_port.py
@@ -0,0 +1,18 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import logging
+from typing import Optional, Union
+
+module_logger = logging.getLogger(__name__)
+
+
+class InputPort:
+    def __init__(self, *, type_string: str, default: Optional[str] = None, optional: Optional[bool] = False):
+        self.type_string = type_string
+        self.optional = optional
+        if self.type_string == "number" and default is not None:
+            self.default: Union[float, Optional[str]] = float(default)
+        else:
+            self.default = default
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job.py
new file mode 100644
index 00000000..b181636e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job.py
@@ -0,0 +1,363 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import json
+import logging
+import traceback
+from abc import abstractmethod
+from collections import OrderedDict
+from os import PathLike
+from pathlib import Path
+from typing import IO, Any, AnyStr, Dict, List, Optional, Tuple, Type, Union
+
+from azure.ai.ml._restclient.runhistory.models import Run
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase, JobService
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobType as RestJobType
+from azure.ai.ml._restclient.v2024_01_01_preview.models import JobBase as JobBase_2401
+from azure.ai.ml._restclient.v2024_01_01_preview.models import JobType as RestJobType_20240101Preview
+from azure.ai.ml._utils._html_utils import make_link, to_html
+from azure.ai.ml._utils.utils import dump_yaml_to_file
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, PARAMS_OVERRIDE_KEY, CommonYamlFields
+from azure.ai.ml.constants._compute import ComputeType
+from azure.ai.ml.constants._job.job import JobServices, JobType
+from azure.ai.ml.entities._mixins import TelemetryMixin
+from azure.ai.ml.entities._resource import Resource
+from azure.ai.ml.entities._util import find_type_in_override
+from azure.ai.ml.exceptions import (
+    ErrorCategory,
+    ErrorTarget,
+    JobException,
+    JobParsingError,
+    PipelineChildJobError,
+    ValidationErrorType,
+    ValidationException,
+)
+
+from ._studio_url_from_job_id import studio_url_from_job_id
+from .pipeline._component_translatable import ComponentTranslatableMixin
+
+module_logger = logging.getLogger(__name__)
+
+
+def _is_pipeline_child_job(job: JobBase) -> bool:
+    # pipeline child job has no properties, so we can check through testing job.properties
+    # if backend has spec changes, this method need to be updated
+    return job.properties is None
+
+
+class Job(Resource, ComponentTranslatableMixin, TelemetryMixin):
+    """Base class for jobs.
+
+    This class should not be instantiated directly. Instead, use one of its subclasses.
+
+    :param name: The name of the job.
+    :type name: Optional[str]
+    :param display_name: The display name of the job.
+    :type display_name: Optional[str]
+    :param description: The description of the job.
+    :type description: Optional[str]
+    :param tags: Tag dictionary. Tags can be added, removed, and updated.
+    :type tags: Optional[dict[str, str]]
+    :param properties: The job property dictionary.
+    :type properties: Optional[dict[str, str]]
+    :param experiment_name: The name of the experiment the job will be created under. Defaults to the name of the
+        current directory.
+    :type experiment_name: Optional[str]
+    :param services: Information on services associated with the job.
+    :type services: Optional[dict[str, ~azure.ai.ml.entities.JobService]]
+    :param compute: Information about the compute resources associated with the job.
+    :type compute: Optional[str]
+    """
+
+    def __init__(
+        self,
+        name: Optional[str] = None,
+        display_name: Optional[str] = None,
+        description: Optional[str] = None,
+        tags: Optional[Dict] = None,
+        properties: Optional[Dict] = None,
+        experiment_name: Optional[str] = None,
+        compute: Optional[str] = None,
+        services: Optional[Dict[str, JobService]] = None,
+        **kwargs: Any,
+    ) -> None:
+        self._type: Optional[str] = kwargs.pop("type", JobType.COMMAND)
+        self._status: Optional[str] = kwargs.pop("status", None)
+        self._log_files: Optional[Dict] = kwargs.pop("log_files", None)
+
+        super().__init__(
+            name=name,
+            description=description,
+            tags=tags,
+            properties=properties,
+            **kwargs,
+        )
+
+        self.display_name = display_name
+        self.experiment_name = experiment_name
+        self.compute: Any = compute
+        self.services = services
+
+    @property
+    def type(self) -> Optional[str]:
+        """The type of the job.
+
+        :return: The type of the job.
+        :rtype: Optional[str]
+        """
+        return self._type
+
+    @property
+    def status(self) -> Optional[str]:
+        """The status of the job.
+
+        Common values returned include "Running", "Completed", and "Failed". All possible values are:
+
+            * NotStarted - This is a temporary state that client-side Run objects are in before cloud submission.
+            * Starting - The Run has started being processed in the cloud. The caller has a run ID at this point.
+            * Provisioning - On-demand compute is being created for a given job submission.
+            * Preparing - The run environment is being prepared and is in one of two stages:
+                * Docker image build
+                * conda environment setup
+            * Queued - The job is queued on the compute target. For example, in BatchAI, the job is in a queued state
+                while waiting for all the requested nodes to be ready.
+            * Running - The job has started to run on the compute target.
+            * Finalizing - User code execution has completed, and the run is in post-processing stages.
+            * CancelRequested - Cancellation has been requested for the job.
+            * Completed - The run has completed successfully. This includes both the user code execution and run
+                post-processing stages.
+            * Failed - The run failed. Usually the Error property on a run will provide details as to why.
+            * Canceled - Follows a cancellation request and indicates that the run is now successfully cancelled.
+            * NotResponding - For runs that have Heartbeats enabled, no heartbeat has been recently sent.
+
+        :return: Status of the job.
+        :rtype: Optional[str]
+        """
+        return self._status
+
+    @property
+    def log_files(self) -> Optional[Dict[str, str]]:
+        """Job output files.
+
+        :return: The dictionary of log names and URLs.
+        :rtype: Optional[Dict[str, str]]
+        """
+        return self._log_files
+
+    @property
+    def studio_url(self) -> Optional[str]:
+        """Azure ML studio endpoint.
+
+        :return: The URL to the job details page.
+        :rtype: Optional[str]
+        """
+        if self.services and (JobServices.STUDIO in self.services.keys()):
+            res: Optional[str] = self.services[JobServices.STUDIO].endpoint
+            return res
+
+        return studio_url_from_job_id(self.id) if self.id else None
+
+    def dump(self, dest: Union[str, PathLike, IO[AnyStr]], **kwargs: Any) -> None:
+        """Dumps the job content into a file in YAML format.
+
+        :param dest: The local path or file stream to write the YAML content to.
+            If dest is a file path, a new file will be created.
+            If dest is an open file, the file will be written to directly.
+        :type dest: Union[PathLike, str, IO[AnyStr]]
+        :raises FileExistsError: Raised if dest is a file path and the file already exists.
+        :raises IOError: Raised if dest is an open file and the file is not writable.
+        """
+        path = kwargs.pop("path", None)
+        yaml_serialized = self._to_dict()
+        dump_yaml_to_file(dest, yaml_serialized, default_flow_style=False, path=path, **kwargs)
+
+    def _get_base_info_dict(self) -> OrderedDict:
+        return OrderedDict(
+            [
+                ("Experiment", self.experiment_name),
+                ("Name", self.name),
+                ("Type", self._type),
+                ("Status", self._status),
+            ]
+        )
+
+    def _repr_html_(self) -> str:
+        info = self._get_base_info_dict()
+        if self.studio_url:
+            info.update(
+                [
+                    (
+                        "Details Page",
+                        make_link(self.studio_url, "Link to Azure Machine Learning studio"),
+                    ),
+                ]
+            )
+        res: str = to_html(info)
+        return res
+
+    @abstractmethod
+    def _to_dict(self) -> Dict:
+        pass
+
+    @classmethod
+    def _resolve_cls_and_type(cls, data: Dict, params_override: Optional[List[Dict]] = None) -> Tuple:
+        from azure.ai.ml.entities._builders.command import Command
+        from azure.ai.ml.entities._builders.spark import Spark
+        from azure.ai.ml.entities._job.automl.automl_job import AutoMLJob
+        from azure.ai.ml.entities._job.distillation.distillation_job import DistillationJob
+        from azure.ai.ml.entities._job.finetuning.finetuning_job import FineTuningJob
+        from azure.ai.ml.entities._job.import_job import ImportJob
+        from azure.ai.ml.entities._job.pipeline.pipeline_job import PipelineJob
+        from azure.ai.ml.entities._job.sweep.sweep_job import SweepJob
+
+        job_type: Optional[Type["Job"]] = None
+        type_in_override = find_type_in_override(params_override)
+        type_str = type_in_override or data.get(CommonYamlFields.TYPE, JobType.COMMAND)  # override takes the priority
+        if type_str == JobType.COMMAND:
+            job_type = Command
+        elif type_str == JobType.SPARK:
+            job_type = Spark
+        elif type_str == JobType.IMPORT:
+            job_type = ImportJob
+        elif type_str == JobType.SWEEP:
+            job_type = SweepJob
+        elif type_str == JobType.AUTOML:
+            job_type = AutoMLJob
+        elif type_str == JobType.PIPELINE:
+            job_type = PipelineJob
+        elif type_str == JobType.FINE_TUNING:
+            job_type = FineTuningJob
+        elif type_str == JobType.DISTILLATION:
+            job_type = DistillationJob
+        else:
+            msg = f"Unsupported job type: {type_str}."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.INVALID_VALUE,
+            )
+        return job_type, type_str
+
+    @classmethod
+    def _load(
+        cls,
+        data: Optional[Dict] = None,
+        yaml_path: Optional[Union[PathLike, str]] = None,
+        params_override: Optional[list] = None,
+        **kwargs: Any,
+    ) -> "Job":
+        """Load a job object from a yaml file.
+
+        :param cls: Indicates that this is a class method.
+        :type cls: class
+        :param data: Data Dictionary, defaults to None
+        :type data: Dict
+        :param yaml_path: YAML Path, defaults to None
+        :type yaml_path: Union[PathLike, str]
+        :param params_override: Fields to overwrite on top of the yaml file.
+            Format is [{"field1": "value1"}, {"field2": "value2"}], defaults to None
+        :type params_override: List[Dict]
+        :raises Exception: An exception
+        :return: Loaded job object.
+        :rtype: Job
+        """
+        data = data or {}
+        params_override = params_override or []
+        context = {
+            BASE_PATH_CONTEXT_KEY: Path(yaml_path).parent if yaml_path else Path("./"),
+            PARAMS_OVERRIDE_KEY: params_override,
+        }
+        job_type, type_str = cls._resolve_cls_and_type(data, params_override)
+        job: Job = job_type._load_from_dict(
+            data=data,
+            context=context,
+            additional_message=f"If you are trying to configure a job that is not of type {type_str}, please specify "
+            f"the correct job type in the 'type' property.",
+            **kwargs,
+        )
+        if yaml_path:
+            job._source_path = yaml_path
+        return job
+
+    @classmethod
+    def _from_rest_object(  # pylint: disable=too-many-return-statements
+        cls, obj: Union[JobBase, JobBase_2401, Run]
+    ) -> "Job":
+        from azure.ai.ml.entities import PipelineJob
+        from azure.ai.ml.entities._builders.command import Command
+        from azure.ai.ml.entities._builders.spark import Spark
+        from azure.ai.ml.entities._job.automl.automl_job import AutoMLJob
+        from azure.ai.ml.entities._job.base_job import _BaseJob
+        from azure.ai.ml.entities._job.distillation.distillation_job import DistillationJob
+        from azure.ai.ml.entities._job.finetuning.finetuning_job import FineTuningJob
+        from azure.ai.ml.entities._job.import_job import ImportJob
+        from azure.ai.ml.entities._job.sweep.sweep_job import SweepJob
+
+        try:
+            if isinstance(obj, Run):
+                # special handling for child jobs
+                return _BaseJob._load_from_rest(obj)
+            if _is_pipeline_child_job(obj):
+                raise PipelineChildJobError(job_id=obj.id)
+            if obj.properties.job_type == RestJobType.COMMAND:
+                # PrP only until new import job type is ready on MFE in PuP
+                # compute type 'DataFactory' is reserved compute name for 'clusterless' ADF jobs
+                if obj.properties.compute_id and obj.properties.compute_id.endswith("/" + ComputeType.ADF):
+                    return ImportJob._load_from_rest(obj)
+
+                res_command: Job = Command._load_from_rest_job(obj)
+                if hasattr(obj, "name"):
+                    res_command._name = obj.name  # type: ignore[attr-defined]
+                return res_command
+            if obj.properties.job_type == RestJobType.SPARK:
+                res_spark: Job = Spark._load_from_rest_job(obj)
+                if hasattr(obj, "name"):
+                    res_spark._name = obj.name  # type: ignore[attr-defined]
+                return res_spark
+            if obj.properties.job_type == RestJobType.SWEEP:
+                return SweepJob._load_from_rest(obj)
+            if obj.properties.job_type == RestJobType.AUTO_ML:
+                return AutoMLJob._load_from_rest(obj)
+            if obj.properties.job_type == RestJobType_20240101Preview.FINE_TUNING:
+                if obj.properties.properties.get("azureml.enable_distillation", False):
+                    return DistillationJob._load_from_rest(obj)
+                return FineTuningJob._load_from_rest(obj)
+            if obj.properties.job_type == RestJobType.PIPELINE:
+                res_pipeline: Job = PipelineJob._load_from_rest(obj)
+                return res_pipeline
+        except PipelineChildJobError as ex:
+            raise ex
+        except Exception as ex:
+            error_message = json.dumps(obj.as_dict(), indent=2) if obj else None
+            module_logger.info(
+                "Exception: %s.\n%s\nUnable to parse the job resource: %s.\n",
+                ex,
+                traceback.format_exc(),
+                error_message,
+            )
+            raise JobParsingError(
+                message=str(ex),
+                no_personal_data_message=f"Unable to parse a job resource of type:{type(obj).__name__}",
+                error_category=ErrorCategory.SYSTEM_ERROR,
+            ) from ex
+        msg = f"Unsupported job type {obj.properties.job_type}"
+        raise JobException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.JOB,
+            error_category=ErrorCategory.SYSTEM_ERROR,
+        )
+
+    def _get_telemetry_values(self) -> Dict:  # pylint: disable=arguments-differ
+        telemetry_values = {"type": self.type}
+        return telemetry_values
+
+    @classmethod
+    @abstractmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "Job":
+        pass
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_io_mixin.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_io_mixin.py
new file mode 100644
index 00000000..21db73ba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_io_mixin.py
@@ -0,0 +1,37 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+
+from typing import Dict, Union
+
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._job._input_output_helpers import build_input_output
+
+
+class JobIOMixin:
+    @property
+    def inputs(self) -> Dict[str, Union[Input, str, bool, int, float]]:
+        return self._inputs
+
+    @inputs.setter
+    def inputs(self, value: Dict[str, Union[Input, str, bool, int, float]]) -> None:
+        self._inputs: Dict = {}
+        if not value:
+            return
+
+        for input_name, input_value in value.items():
+            self._inputs[input_name] = build_input_output(input_value)
+
+    @property
+    def outputs(self) -> Dict[str, Output]:
+        return self._outputs
+
+    @outputs.setter
+    def outputs(self, value: Dict[str, Output]) -> None:
+        self._outputs: Dict = {}
+        if not value:
+            return
+
+        for output_name, output_value in value.items():
+            self._outputs[output_name] = build_input_output(output_value, inputs=False)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_limits.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_limits.py
new file mode 100644
index 00000000..7aae9263
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_limits.py
@@ -0,0 +1,201 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import logging
+from abc import ABC
+from typing import Any, Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import CommandJobLimits as RestCommandJobLimits
+from azure.ai.ml._restclient.v2023_08_01_preview.models import SweepJobLimits as RestSweepJobLimits
+from azure.ai.ml._utils.utils import from_iso_duration_format, is_data_binding_expression, to_iso_duration_format
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+module_logger = logging.getLogger(__name__)
+
+
+class JobLimits(RestTranslatableMixin, ABC):
+    """Base class for Job limits.
+
+    This class should not be instantiated directly. Instead, one of its child classes should be used.
+    """
+
+    def __init__(
+        self,
+    ) -> None:
+        self.type: Any = None
+
+    def __eq__(self, other: Any) -> bool:
+        if not isinstance(other, JobLimits):
+            return NotImplemented
+        res: bool = self._to_rest_object() == other._to_rest_object()
+        return res
+
+
+class CommandJobLimits(JobLimits):
+    """Limits for Command Jobs.
+
+    :keyword timeout: The maximum run duration, in seconds, after which the job will be cancelled.
+    :paramtype timeout: Optional[Union[int, str]]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_command_configurations.py
+            :start-after: [START command_job_definition]
+            :end-before: [END command_job_definition]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a CommandJob with CommandJobLimits.
+    """
+
+    def __init__(self, *, timeout: Optional[Union[int, str]] = None) -> None:
+        super().__init__()
+        self.type = JobType.COMMAND
+        self.timeout = timeout
+
+    def _to_rest_object(self) -> RestCommandJobLimits:
+        if is_data_binding_expression(self.timeout):
+            return RestCommandJobLimits(timeout=self.timeout)
+        return RestCommandJobLimits(timeout=to_iso_duration_format(self.timeout))
+
+    @classmethod
+    def _from_rest_object(cls, obj: Union[RestCommandJobLimits, dict]) -> Optional["CommandJobLimits"]:
+        if not obj:
+            return None
+        if isinstance(obj, dict):
+            timeout_value = obj.get("timeout", None)
+            # if timeout value is a binding string
+            if is_data_binding_expression(timeout_value):
+                return cls(timeout=timeout_value)
+            # if response timeout is a normal iso date string
+            obj = RestCommandJobLimits.from_dict(obj)
+        return cls(timeout=from_iso_duration_format(obj.timeout))
+
+
+class SweepJobLimits(JobLimits):
+    """Limits for Sweep Jobs.
+
+    :keyword max_concurrent_trials: The maximum number of concurrent trials for the Sweep Job.
+    :paramtype max_concurrent_trials: Optional[int]
+    :keyword max_total_trials: The maximum number of total trials for the Sweep Job.
+    :paramtype max_total_trials: Optional[int]
+    :keyword timeout: The maximum run duration, in seconds, after which the job will be cancelled.
+    :paramtype timeout: Optional[int]
+    :keyword trial_timeout: The timeout value, in seconds, for each Sweep Job trial.
+    :paramtype trial_timeout: Optional[int]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_bayesian_sampling_algorithm]
+            :end-before: [END configure_sweep_job_bayesian_sampling_algorithm]
+            :language: python
+            :dedent: 8
+            :caption: Assigning limits to a SweepJob
+    """
+
+    def __init__(
+        self,
+        *,
+        max_concurrent_trials: Optional[int] = None,
+        max_total_trials: Optional[int] = None,
+        timeout: Optional[int] = None,
+        trial_timeout: Optional[Union[int, str]] = None,
+    ) -> None:
+        super().__init__()
+        self.type = JobType.SWEEP
+        self.max_concurrent_trials = max_concurrent_trials
+        self.max_total_trials = max_total_trials
+        self._timeout = _get_floored_timeout(timeout)
+        self._trial_timeout = _get_floored_timeout(trial_timeout)
+
+    @property
+    def timeout(self) -> Optional[Union[int, str]]:
+        """The maximum run duration, in seconds, after which the job will be cancelled.
+
+        :return: The maximum run duration, in seconds, after which the job will be cancelled.
+        :rtype: int
+        """
+        return self._timeout
+
+    @timeout.setter
+    def timeout(self, value: int) -> None:
+        """Sets the maximum run duration.
+
+        :param value: The maximum run duration, in seconds, after which the job will be cancelled.
+        :type value: int
+        """
+        self._timeout = _get_floored_timeout(value)
+
+    @property
+    def trial_timeout(self) -> Optional[Union[int, str]]:
+        """The timeout value, in seconds, for each Sweep Job trial.
+
+        :return: The timeout value, in seconds, for each Sweep Job trial.
+        :rtype: int
+        """
+        return self._trial_timeout
+
+    @trial_timeout.setter
+    def trial_timeout(self, value: int) -> None:
+        """Sets the timeout value for each Sweep Job trial.
+
+        :param value: The timeout value, in seconds, for each Sweep Job trial.
+        :type value: int
+        """
+        self._trial_timeout = _get_floored_timeout(value)
+
+    def _to_rest_object(self) -> RestSweepJobLimits:
+        return RestSweepJobLimits(
+            max_concurrent_trials=self.max_concurrent_trials,
+            max_total_trials=self.max_total_trials,
+            timeout=to_iso_duration_format(self.timeout),
+            trial_timeout=to_iso_duration_format(self.trial_timeout),
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestSweepJobLimits) -> Optional["SweepJobLimits"]:
+        if not obj:
+            return None
+
+        return cls(
+            max_concurrent_trials=obj.max_concurrent_trials,
+            max_total_trials=obj.max_total_trials,
+            timeout=from_iso_duration_format(obj.timeout),
+            trial_timeout=from_iso_duration_format(obj.trial_timeout),
+        )
+
+
+def _get_floored_timeout(value: Optional[Union[int, str]]) -> Optional[Union[int, str]]:
+    # Bug 1335978:  Service rounds durations less than 60 seconds to 60 days.
+    # If duration is non-0 and less than 60, set to 60.
+    if isinstance(value, int):
+        return value if not value or value > 60 else 60
+
+    return None
+
+
+class DoWhileJobLimits(JobLimits):
+    """DoWhile Job limit class.
+
+    :keyword max_iteration_count: The maximum number of iterations for the DoWhile Job.
+    :paramtype max_iteration_count: Optional[int]
+    """
+
+    def __init__(
+        self,  # pylint: disable=unused-argument
+        *,
+        max_iteration_count: Optional[int] = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__()
+        self._max_iteration_count = max_iteration_count
+
+    @property
+    def max_iteration_count(self) -> Optional[int]:
+        """The maximum number of iterations for the DoWhile Job.
+
+        :rtype: int
+        """
+        return self._max_iteration_count
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_name_generator.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_name_generator.py
new file mode 100644
index 00000000..e4f62d3d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_name_generator.py
@@ -0,0 +1,487 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import random
+
+SUFFIX_LENGTH = 10
+ALLOWED_CHARS = "bcdfghjklmnpqrstvwxyz0123456789"
+
+ALLOWED_ADJECTIVES = [
+    "affable",
+    "amiable",
+    "amusing",
+    "ashy",
+    "blue",
+    "bold",
+    "boring",
+    "brave",
+    "bright",
+    "bubbly",
+    "busy",
+    "calm",
+    "careful",
+    "clever",
+    "cool",
+    "coral",
+    "crimson",
+    "cyan",
+    "dreamy",
+    "dynamic",
+    "eager",
+    "elated",
+    "epic",
+    "frank",
+    "frosty",
+    "funny",
+    "gentle",
+    "gifted",
+    "good",
+    "goofy",
+    "gray",
+    "great",
+    "green",
+    "happy",
+    "helpful",
+    "heroic",
+    "honest",
+    "hungry",
+    "icy",
+    "ivory",
+    "jolly",
+    "jovial",
+    "joyful",
+    "keen",
+    "khaki",
+    "kind",
+    "lemon",
+    "lime",
+    "loving",
+    "loyal",
+    "lucid",
+    "magenta",
+    "mango",
+    "maroon",
+    "mighty",
+    "modest",
+    "musing",
+    "neat",
+    "nice",
+    "nifty",
+    "olden",
+    "olive",
+    "orange",
+    "patient",
+    "placid",
+    "plucky",
+    "plum",
+    "polite",
+    "purple",
+    "quiet",
+    "quirky",
+    "red",
+    "sad",
+    "salmon",
+    "serene",
+    "sharp",
+    "shy",
+    "silly",
+    "silver",
+    "sincere",
+    "sleepy",
+    "stoic",
+    "strong",
+    "sweet",
+    "teal",
+    "tender",
+    "tidy",
+    "tough",
+    "upbeat",
+    "wheat",
+    "willing",
+    "witty",
+    "yellow",
+    "zen",
+]
+
+ALLOWED_NOUNS = [
+    "actor",
+    "airport",
+    "angle",
+    "animal",
+    "answer",
+    "ant",
+    "apple",
+    "apricot",
+    "arch",
+    "arm",
+    "atemoya",
+    "avocado",
+    "bag",
+    "ball",
+    "balloon",
+    "band",
+    "basil",
+    "basin",
+    "basket",
+    "battery",
+    "beach",
+    "bean",
+    "bear",
+    "beard",
+    "bee",
+    "beet",
+    "bell",
+    "berry",
+    "bird",
+    "board",
+    "boat",
+    "bone",
+    "boniato",
+    "book",
+    "boot",
+    "bottle",
+    "box",
+    "brain",
+    "brake",
+    "branch",
+    "bread",
+    "brick",
+    "bridge",
+    "brush",
+    "bucket",
+    "bulb",
+    "button",
+    "cabbage",
+    "cake",
+    "calypso",
+    "camel",
+    "camera",
+    "candle",
+    "car",
+    "caravan",
+    "card",
+    "carnival",
+    "carpet",
+    "carrot",
+    "cart",
+    "cartoon",
+    "cassava",
+    "cat",
+    "celery",
+    "chaconia",
+    "chain",
+    "chayote",
+    "cheese",
+    "cheetah",
+    "cherry",
+    "chicken",
+    "chin",
+    "circle",
+    "clock",
+    "cloud",
+    "coat",
+    "coconut",
+    "collar",
+    "comb",
+    "cord",
+    "corn",
+    "cow",
+    "crayon",
+    "crowd",
+    "cumin",
+    "cup",
+    "curtain",
+    "cushion",
+    "date",
+    "deer",
+    "diamond",
+    "dinner",
+    "dog",
+    "dolphin",
+    "door",
+    "double",
+    "drain",
+    "drawer",
+    "dream",
+    "dress",
+    "drop",
+    "duck",
+    "eagle",
+    "ear",
+    "egg",
+    "endive",
+    "energy",
+    "engine",
+    "evening",
+    "eye",
+    "farm",
+    "feast",
+    "feather",
+    "feijoa",
+    "fennel",
+    "fig",
+    "fish",
+    "flag",
+    "floor",
+    "flower",
+    "fly",
+    "foot",
+    "forest",
+    "fork",
+    "fowl",
+    "fox",
+    "frame",
+    "frog",
+    "garage",
+    "garden",
+    "garlic",
+    "gas",
+    "ghost",
+    "giraffe",
+    "glass",
+    "glove",
+    "goat",
+    "gold",
+    "grape",
+    "grass",
+    "guava",
+    "guitar",
+    "gyro",
+    "hair",
+    "hamster",
+    "hand",
+    "hat",
+    "head",
+    "heart",
+    "helmet",
+    "holiday",
+    "hominy",
+    "honey",
+    "hook",
+    "horse",
+    "house",
+    "ice",
+    "insect",
+    "iron",
+    "island",
+    "jackal",
+    "jelly",
+    "jewel",
+    "jicama",
+    "juice",
+    "kale",
+    "kettle",
+    "key",
+    "king",
+    "kitchen",
+    "kite",
+    "kitten",
+    "kiwi",
+    "knee",
+    "knot",
+    "kumquat",
+    "lamp",
+    "leaf",
+    "leather",
+    "leek",
+    "leg",
+    "lemon",
+    "lettuce",
+    "library",
+    "lime",
+    "line",
+    "lion",
+    "lizard",
+    "lobster",
+    "lock",
+    "longan",
+    "loquat",
+    "lunch",
+    "lychee",
+    "machine",
+    "malanga",
+    "mango",
+    "mangos",
+    "map",
+    "market",
+    "match",
+    "melon",
+    "milk",
+    "monkey",
+    "moon",
+    "morning",
+    "muscle",
+    "music",
+    "nail",
+    "napa",
+    "napkin",
+    "neck",
+    "needle",
+    "nerve",
+    "nest",
+    "net",
+    "night",
+    "nose",
+    "nut",
+    "nutmeg",
+    "ocean",
+    "octopus",
+    "office",
+    "oil",
+    "okra",
+    "onion",
+    "orange",
+    "oregano",
+    "oven",
+    "owl",
+    "oxygen",
+    "oyster",
+    "panda",
+    "papaya",
+    "parang",
+    "parcel",
+    "parrot",
+    "parsnip",
+    "pasta",
+    "pea",
+    "peach",
+    "pear",
+    "pen",
+    "pencil",
+    "pepper",
+    "piano",
+    "picture",
+    "pig",
+    "pillow",
+    "pin",
+    "pipe",
+    "pizza",
+    "plane",
+    "planet",
+    "plastic",
+    "plate",
+    "plow",
+    "plum",
+    "pocket",
+    "pot",
+    "potato",
+    "prune",
+    "pummelo",
+    "pump",
+    "pumpkin",
+    "puppy",
+    "queen",
+    "quill",
+    "quince",
+    "rabbit",
+    "rail",
+    "rain",
+    "rainbow",
+    "raisin",
+    "rat",
+    "receipt",
+    "reggae",
+    "rhubarb",
+    "rhythm",
+    "rice",
+    "ring",
+    "river",
+    "rocket",
+    "rod",
+    "roof",
+    "room",
+    "root",
+    "rose",
+    "roti",
+    "sail",
+    "salt",
+    "sand",
+    "school",
+    "scooter",
+    "screw",
+    "seal",
+    "seed",
+    "shampoo",
+    "shark",
+    "sheep",
+    "shelf",
+    "ship",
+    "shirt",
+    "shoe",
+    "skin",
+    "snail",
+    "snake",
+    "soca",
+    "soccer",
+    "sock",
+    "soursop",
+    "spade",
+    "spider",
+    "spinach",
+    "sponge",
+    "spoon",
+    "spring",
+    "sprout",
+    "square",
+    "squash",
+    "stamp",
+    "star",
+    "station",
+    "steelpan",
+    "stem",
+    "stick",
+    "stomach",
+    "stone",
+    "store",
+    "street",
+    "sugar",
+    "sun",
+    "table",
+    "tail",
+    "tangelo",
+    "tent",
+    "thread",
+    "ticket",
+    "tiger",
+    "toe",
+    "tomato",
+    "tongue",
+    "tooth",
+    "town",
+    "train",
+    "tray",
+    "tree",
+    "truck",
+    "turnip",
+    "turtle",
+    "van",
+    "vase",
+    "vinegar",
+    "vulture",
+    "wall",
+    "watch",
+    "whale",
+    "wheel",
+    "whistle",
+    "window",
+    "wing",
+    "wire",
+    "wolf",
+    "worm",
+    "yacht",
+    "yak",
+    "yam",
+    "yogurt",
+    "yuca",
+    "zebra",
+    "zoo",
+]
+
+
+def generate_job_name() -> str:
+    adj = random.choice(ALLOWED_ADJECTIVES)
+    noun = random.choice(ALLOWED_NOUNS)
+    suffix = "".join(random.choices(ALLOWED_CHARS, k=SUFFIX_LENGTH))
+
+    return "_".join([adj, noun, suffix])
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_resource_configuration.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_resource_configuration.py
new file mode 100644
index 00000000..a27b5ba1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_resource_configuration.py
@@ -0,0 +1,239 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import json
+import logging
+from typing import Any, Dict, List, Optional, Union, cast
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobResourceConfiguration as RestJobResourceConfiguration
+from azure.ai.ml._restclient.v2025_01_01_preview.models import (
+    JobResourceConfiguration as RestJobResourceConfiguration202501,
+)
+from azure.ai.ml.constants._job.job import JobComputePropertyFields
+from azure.ai.ml.entities._mixins import DictMixin, RestTranslatableMixin
+from azure.ai.ml.entities._util import convert_ordered_dict_to_dict
+
+module_logger = logging.getLogger(__name__)
+
+
+class BaseProperty(dict):
+    """Base class for entity classes to be used as value of JobResourceConfiguration.properties."""
+
+    def __init__(self, **kwargs: Any) -> None:
+        super().__init__()
+        for key, value in kwargs.items():
+            setattr(self, key, value)
+
+    def __setattr__(self, key: str, value: Any) -> None:
+        if key.startswith("_"):
+            super().__setattr__(key, value)
+        else:
+            self[key] = value
+
+    def __getattr__(self, key: str) -> Any:
+        if key.startswith("_"):
+            super().__getattribute__(key)
+            return None
+
+        return self[key]
+
+    def __repr__(self) -> str:
+        return json.dumps(self.as_dict())
+
+    def __eq__(self, other: Any) -> bool:
+        if isinstance(other, dict):
+            return self.as_dict() == other
+        if isinstance(other, BaseProperty):
+            return self.as_dict() == other.as_dict()
+        return False
+
+    def as_dict(self) -> Dict[str, Any]:
+        res: dict = self._to_dict(self)
+        return res
+
+    @classmethod
+    def _to_dict(cls, obj: Any) -> Any:
+        if isinstance(obj, dict):
+            result = {}
+            for key, value in obj.items():
+                if value is None:
+                    continue
+                if isinstance(value, dict):
+                    result[key] = cls._to_dict(value)
+                else:
+                    result[key] = value
+            return result
+        return obj
+
+
+class Properties(BaseProperty):
+    # pre-defined properties are case-insensitive
+    # Map Singularity -> AISupercomputer in SDK until MFE does mapping
+    _KEY_MAPPING = {
+        JobComputePropertyFields.AISUPERCOMPUTER.lower(): JobComputePropertyFields.AISUPERCOMPUTER,
+        JobComputePropertyFields.SINGULARITY.lower(): JobComputePropertyFields.AISUPERCOMPUTER,
+        JobComputePropertyFields.ITP.lower(): JobComputePropertyFields.ITP,
+        JobComputePropertyFields.TARGET_SELECTOR.lower(): JobComputePropertyFields.TARGET_SELECTOR,
+    }
+
+    def as_dict(self) -> Dict[str, Any]:
+        result = {}
+        for key, value in super().as_dict().items():
+            if key.lower() in self._KEY_MAPPING:
+                key = self._KEY_MAPPING[key.lower()]
+            result[key] = value
+        # recursively convert Ordered Dict to dictionary
+        return cast(dict, convert_ordered_dict_to_dict(result))
+
+
+class JobResourceConfiguration(RestTranslatableMixin, DictMixin):
+    """Job resource configuration class, inherited and extended functionalities from ResourceConfiguration.
+
+    :keyword locations: A list of locations where the job can run.
+    :paramtype locations: Optional[List[str]]
+    :keyword instance_count: The number of instances or nodes used by the compute target.
+    :paramtype instance_count: Optional[int]
+    :keyword instance_type: The type of VM to be used, as supported by the compute target.
+    :paramtype instance_type: Optional[str]
+    :keyword properties: A dictionary of properties for the job.
+    :paramtype properties: Optional[dict[str, Any]]
+    :keyword docker_args: Extra arguments to pass to the Docker run command. This would override any
+        parameters that have already been set by the system, or in this section. This parameter is only
+        supported for Azure ML compute types.
+    :paramtype docker_args: Optional[Union[str, List[str]]]
+    :keyword shm_size: The size of the docker container's shared memory block. This should be in the
+        format of (number)(unit) where the number has to be greater than 0 and the unit can be one of
+        b(bytes), k(kilobytes), m(megabytes), or g(gigabytes).
+    :paramtype shm_size: Optional[str]
+    :keyword max_instance_count: The maximum number of instances or nodes used by the compute target.
+    :paramtype max_instance_count: Optional[int]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_command_configurations.py
+            :start-after: [START command_job_resource_configuration]
+            :end-before: [END command_job_resource_configuration]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a CommandJob with a JobResourceConfiguration.
+    """
+
+    def __init__(
+        self,  # pylint: disable=unused-argument
+        *,
+        locations: Optional[List[str]] = None,
+        instance_count: Optional[int] = None,
+        instance_type: Optional[Union[str, List]] = None,
+        properties: Optional[Union[Properties, Dict]] = None,
+        docker_args: Optional[Union[str, List[str]]] = None,
+        shm_size: Optional[str] = None,
+        max_instance_count: Optional[int] = None,
+        **kwargs: Any
+    ) -> None:
+        self.locations = locations
+        self.instance_count = instance_count
+        self.instance_type = instance_type
+        self.shm_size = shm_size
+        self.max_instance_count = max_instance_count
+        self.docker_args = docker_args
+        self._properties = None
+        self.properties = properties
+
+    @property
+    def properties(self) -> Optional[Union[Properties, Dict]]:
+        """The properties of the job.
+
+        :rtype: ~azure.ai.ml.entities._job.job_resource_configuration.Properties
+        """
+        return self._properties
+
+    @properties.setter
+    def properties(self, properties: Dict[str, Any]) -> None:
+        """Sets the properties of the job.
+
+        :param properties: A dictionary of properties for the job.
+        :type properties: Dict[str, Any]
+        :raises TypeError: Raised if properties is not a dictionary type.
+        """
+        if properties is None:
+            self._properties = Properties()
+        elif isinstance(properties, dict):
+            self._properties = Properties(**properties)
+        else:
+            raise TypeError("properties must be a dict.")
+
+    def _to_rest_object(self) -> Union[RestJobResourceConfiguration, RestJobResourceConfiguration202501]:
+        if self.docker_args and isinstance(self.docker_args, list):
+            return RestJobResourceConfiguration202501(
+                instance_count=self.instance_count,
+                instance_type=self.instance_type,
+                max_instance_count=self.max_instance_count,
+                properties=self.properties.as_dict() if isinstance(self.properties, Properties) else None,
+                docker_args_list=self.docker_args,
+                shm_size=self.shm_size,
+            )
+        return RestJobResourceConfiguration(
+            locations=self.locations,
+            instance_count=self.instance_count,
+            instance_type=self.instance_type,
+            max_instance_count=self.max_instance_count,
+            properties=self.properties.as_dict() if isinstance(self.properties, Properties) else None,
+            docker_args=self.docker_args,
+            shm_size=self.shm_size,
+        )
+
+    @classmethod
+    def _from_rest_object(
+        cls, obj: Optional[Union[RestJobResourceConfiguration, RestJobResourceConfiguration202501]]
+    ) -> Optional["JobResourceConfiguration"]:
+        if obj is None:
+            return None
+        if isinstance(obj, dict):
+            return cls(**obj)
+        return JobResourceConfiguration(
+            locations=obj.locations if hasattr(obj, "locations") else None,
+            instance_count=obj.instance_count,
+            instance_type=obj.instance_type,
+            max_instance_count=obj.max_instance_count if hasattr(obj, "max_instance_count") else None,
+            properties=obj.properties,
+            docker_args=obj.docker_args_list if hasattr(obj, "docker_args_list") else obj.docker_args,
+            shm_size=obj.shm_size,
+            deserialize_properties=True,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, JobResourceConfiguration):
+            return NotImplemented
+        return (
+            self.locations == other.locations
+            and self.instance_count == other.instance_count
+            and self.instance_type == other.instance_type
+            and self.max_instance_count == other.max_instance_count
+            and self.docker_args == other.docker_args
+            and self.shm_size == other.shm_size
+        )
+
+    def __ne__(self, other: object) -> bool:
+        if not isinstance(other, JobResourceConfiguration):
+            return NotImplemented
+        return not self.__eq__(other)
+
+    def _merge_with(self, other: "JobResourceConfiguration") -> None:
+        if other:
+            if other.locations:
+                self.locations = other.locations
+            if other.instance_count:
+                self.instance_count = other.instance_count
+            if other.instance_type:
+                self.instance_type = other.instance_type
+            if other.max_instance_count:
+                self.max_instance_count = other.max_instance_count
+            if other.properties:
+                self.properties = other.properties
+            if other.docker_args:
+                self.docker_args = other.docker_args
+            if other.shm_size:
+                self.shm_size = other.shm_size
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_resources.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_resources.py
new file mode 100644
index 00000000..bd1cdad5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_resources.py
@@ -0,0 +1,33 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Any, List
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+from azure.ai.ml._restclient.v2024_10_01_preview.models import JobResources as RestJobResources
+
+
+class JobResources(RestTranslatableMixin):
+    """Resource configuration for a job.
+
+    This class should not be instantiated directly. Instead, use its subclasses.
+    """
+
+    def __init__(self, *, instance_types: List[str]) -> None:
+        self.instance_types = instance_types
+
+    def _to_rest_object(self) -> Any:
+        return RestJobResources(instance_types=self.instance_types)
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestJobResources) -> "JobResources":
+        job_resources = cls(instance_types=obj.instance_types)
+        return job_resources
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, JobResources):
+            return NotImplemented
+        return self.instance_types == other.instance_types
+
+    def __ne__(self, other: object) -> bool:
+        return not self.__eq__(other)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_service.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_service.py
new file mode 100644
index 00000000..a97048fc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/job_service.py
@@ -0,0 +1,424 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import logging
+from typing import Any, Dict, Optional, cast
+
+from typing_extensions import Literal
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import AllNodes
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobService as RestJobService
+from azure.ai.ml.constants._job.job import JobServiceTypeNames
+from azure.ai.ml.entities._mixins import DictMixin, RestTranslatableMixin
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException
+
+module_logger = logging.getLogger(__name__)
+
+
+class JobServiceBase(RestTranslatableMixin, DictMixin):
+    """Base class for job service configuration.
+
+    This class should not be instantiated directly. Instead, use one of its subclasses.
+
+    :keyword endpoint: The endpoint URL.
+    :paramtype endpoint: Optional[str]
+    :keyword type: The endpoint type. Accepted values are "jupyter_lab", "ssh", "tensor_board", and "vs_code".
+    :paramtype type: Optional[Literal["jupyter_lab", "ssh", "tensor_board", "vs_code"]]
+    :keyword port: The port for the endpoint.
+    :paramtype port: Optional[int]
+    :keyword nodes: Indicates whether the service has to run in all nodes.
+    :paramtype nodes: Optional[Literal["all"]]
+    :keyword properties: Additional properties to set on the endpoint.
+    :paramtype properties: Optional[dict[str, str]]
+    :keyword status: The status of the endpoint.
+    :paramtype status: Optional[str]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+    """
+
+    def __init__(  # pylint: disable=unused-argument
+        self,
+        *,
+        endpoint: Optional[str] = None,
+        type: Optional[  # pylint: disable=redefined-builtin
+            Literal["jupyter_lab", "ssh", "tensor_board", "vs_code"]
+        ] = None,
+        nodes: Optional[Literal["all"]] = None,
+        status: Optional[str] = None,
+        port: Optional[int] = None,
+        properties: Optional[Dict[str, str]] = None,
+        **kwargs: Dict,
+    ) -> None:
+        self.endpoint = endpoint
+        self.type: Any = type
+        self.nodes = nodes
+        self.status = status
+        self.port = port
+        self.properties = properties
+        self._validate_nodes()
+        self._validate_type_name()
+
+    def _validate_nodes(self) -> None:
+        if not self.nodes in ["all", None]:
+            msg = f"nodes should be either 'all' or None, but received '{self.nodes}'."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.INVALID_VALUE,
+            )
+
+    def _validate_type_name(self) -> None:
+        if self.type and not self.type in JobServiceTypeNames.ENTITY_TO_REST:
+            msg = (
+                f"type should be one of " f"{JobServiceTypeNames.NAMES_ALLOWED_FOR_PUBLIC}, but received '{self.type}'."
+            )
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.INVALID_VALUE,
+            )
+
+    def _to_rest_job_service(self, updated_properties: Optional[Dict[str, str]] = None) -> RestJobService:
+        return RestJobService(
+            endpoint=self.endpoint,
+            job_service_type=JobServiceTypeNames.ENTITY_TO_REST.get(self.type, None) if self.type else None,
+            nodes=AllNodes() if self.nodes else None,
+            status=self.status,
+            port=self.port,
+            properties=updated_properties if updated_properties else self.properties,
+        )
+
+    @classmethod
+    def _to_rest_job_services(
+        cls,
+        services: Optional[Dict],
+    ) -> Optional[Dict[str, RestJobService]]:
+        if services is None:
+            return None
+
+        return {name: service._to_rest_object() for name, service in services.items()}
+
+    @classmethod
+    def _from_rest_job_service_object(cls, obj: RestJobService) -> "JobServiceBase":
+        return cls(
+            endpoint=obj.endpoint,
+            type=(
+                JobServiceTypeNames.REST_TO_ENTITY.get(obj.job_service_type, None)  # type: ignore[arg-type]
+                if obj.job_service_type
+                else None
+            ),
+            nodes="all" if obj.nodes else None,
+            status=obj.status,
+            port=obj.port,
+            # ssh_public_keys=_get_property(obj.properties, "sshPublicKeys"),
+            properties=obj.properties,
+        )
+
+    @classmethod
+    def _from_rest_job_services(cls, services: Dict[str, RestJobService]) -> Dict:
+        # """Resolve Dict[str, RestJobService] to Dict[str, Specific JobService]"""
+        if services is None:
+            return None
+
+        result: dict = {}
+        for name, service in services.items():
+            if service.job_service_type == JobServiceTypeNames.RestNames.JUPYTER_LAB:
+                result[name] = JupyterLabJobService._from_rest_object(service)
+            elif service.job_service_type == JobServiceTypeNames.RestNames.SSH:
+                result[name] = SshJobService._from_rest_object(service)
+            elif service.job_service_type == JobServiceTypeNames.RestNames.TENSOR_BOARD:
+                result[name] = TensorBoardJobService._from_rest_object(service)
+            elif service.job_service_type == JobServiceTypeNames.RestNames.VS_CODE:
+                result[name] = VsCodeJobService._from_rest_object(service)
+            else:
+                result[name] = JobService._from_rest_object(service)
+        return result
+
+
+class JobService(JobServiceBase):
+    """Basic job service configuration for backward compatibility.
+
+    This class is not intended to be used directly. Instead, use one of its subclasses specific to your job type.
+
+    :keyword endpoint: The endpoint URL.
+    :paramtype endpoint: Optional[str]
+    :keyword type: The endpoint type. Accepted values are "jupyter_lab", "ssh", "tensor_board", and "vs_code".
+    :paramtype type: Optional[Literal["jupyter_lab", "ssh", "tensor_board", "vs_code"]]
+    :keyword port: The port for the endpoint.
+    :paramtype port: Optional[int]
+    :keyword nodes: Indicates whether the service has to run in all nodes.
+    :paramtype nodes: Optional[Literal["all"]]
+    :keyword properties: Additional properties to set on the endpoint.
+    :paramtype properties: Optional[dict[str, str]]
+    :keyword status: The status of the endpoint.
+    :paramtype status: Optional[str]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+    """
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestJobService) -> "JobService":
+        return cast(JobService, cls._from_rest_job_service_object(obj))
+
+    def _to_rest_object(self) -> RestJobService:
+        return self._to_rest_job_service()
+
+
+class SshJobService(JobServiceBase):
+    """SSH job service configuration.
+
+    :ivar type: Specifies the type of job service. Set automatically to "ssh" for this class.
+    :vartype type: str
+    :keyword endpoint: The endpoint URL.
+    :paramtype endpoint: Optional[str]
+    :keyword port: The port for the endpoint.
+    :paramtype port: Optional[int]
+    :keyword nodes: Indicates whether the service has to run in all nodes.
+    :paramtype nodes: Optional[Literal["all"]]
+    :keyword properties: Additional properties to set on the endpoint.
+    :paramtype properties: Optional[dict[str, str]]
+    :keyword status: The status of the endpoint.
+    :paramtype status: Optional[str]
+    :keyword ssh_public_keys: The SSH Public Key to access the job container.
+    :paramtype ssh_public_keys: Optional[str]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_misc.py
+            :start-after: [START ssh_job_service_configuration]
+            :end-before: [END ssh_job_service_configuration]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a SshJobService configuration on a command job.
+    """
+
+    def __init__(
+        self,
+        *,
+        endpoint: Optional[str] = None,
+        nodes: Optional[Literal["all"]] = None,
+        status: Optional[str] = None,
+        port: Optional[int] = None,
+        ssh_public_keys: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(
+            endpoint=endpoint,
+            nodes=nodes,
+            status=status,
+            port=port,
+            properties=properties,
+            **kwargs,
+        )
+        self.type = JobServiceTypeNames.EntityNames.SSH
+        self.ssh_public_keys = ssh_public_keys
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestJobService) -> "SshJobService":
+        ssh_job_service = cast(SshJobService, cls._from_rest_job_service_object(obj))
+        ssh_job_service.ssh_public_keys = _get_property(obj.properties, "sshPublicKeys")
+        return ssh_job_service
+
+    def _to_rest_object(self) -> RestJobService:
+        updated_properties = _append_or_update_properties(self.properties, "sshPublicKeys", self.ssh_public_keys)
+        return self._to_rest_job_service(updated_properties)
+
+
+class TensorBoardJobService(JobServiceBase):
+    """TensorBoard job service configuration.
+
+    :ivar type: Specifies the type of job service. Set automatically to "tensor_board" for this class.
+    :vartype type: str
+    :keyword endpoint: The endpoint URL.
+    :paramtype endpoint: Optional[str]
+    :keyword port: The port for the endpoint.
+    :paramtype port: Optional[int]
+    :keyword nodes: Indicates whether the service has to run in all nodes.
+    :paramtype nodes: Optional[Literal["all"]]
+    :keyword properties: Additional properties to set on the endpoint.
+    :paramtype properties: Optional[dict[str, str]]
+    :keyword status: The status of the endpoint.
+    :paramtype status: Optional[str]
+    :keyword log_dir: The directory path for the log file.
+    :paramtype log_dir: Optional[str]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_misc.py
+            :start-after: [START ssh_job_service_configuration]
+            :end-before: [END ssh_job_service_configuration]
+            :language: python
+            :dedent: 8
+            :caption: Configuring TensorBoardJobService configuration on a command job.
+    """
+
+    def __init__(
+        self,
+        *,
+        endpoint: Optional[str] = None,
+        nodes: Optional[Literal["all"]] = None,
+        status: Optional[str] = None,
+        port: Optional[int] = None,
+        log_dir: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(
+            endpoint=endpoint,
+            nodes=nodes,
+            status=status,
+            port=port,
+            properties=properties,
+            **kwargs,
+        )
+        self.type = JobServiceTypeNames.EntityNames.TENSOR_BOARD
+        self.log_dir = log_dir
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestJobService) -> "TensorBoardJobService":
+        tensorboard_job_Service = cast(TensorBoardJobService, cls._from_rest_job_service_object(obj))
+        tensorboard_job_Service.log_dir = _get_property(obj.properties, "logDir")
+        return tensorboard_job_Service
+
+    def _to_rest_object(self) -> RestJobService:
+        updated_properties = _append_or_update_properties(self.properties, "logDir", self.log_dir)
+        return self._to_rest_job_service(updated_properties)
+
+
+class JupyterLabJobService(JobServiceBase):
+    """JupyterLab job service configuration.
+
+    :ivar type: Specifies the type of job service. Set automatically to "jupyter_lab" for this class.
+    :vartype type: str
+    :keyword endpoint: The endpoint URL.
+    :paramtype endpoint: Optional[str]
+    :keyword port: The port for the endpoint.
+    :paramtype port: Optional[int]
+    :keyword nodes: Indicates whether the service has to run in all nodes.
+    :paramtype nodes: Optional[Literal["all"]]
+    :keyword properties: Additional properties to set on the endpoint.
+    :paramtype properties: Optional[dict[str, str]]
+    :keyword status: The status of the endpoint.
+    :paramtype status: Optional[str]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_misc.py
+            :start-after: [START ssh_job_service_configuration]
+            :end-before: [END ssh_job_service_configuration]
+            :language: python
+            :dedent: 8
+            :caption: Configuring JupyterLabJobService configuration on a command job.
+    """
+
+    def __init__(
+        self,
+        *,
+        endpoint: Optional[str] = None,
+        nodes: Optional[Literal["all"]] = None,
+        status: Optional[str] = None,
+        port: Optional[int] = None,
+        properties: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(
+            endpoint=endpoint,
+            nodes=nodes,
+            status=status,
+            port=port,
+            properties=properties,
+            **kwargs,
+        )
+        self.type = JobServiceTypeNames.EntityNames.JUPYTER_LAB
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestJobService) -> "JupyterLabJobService":
+        return cast(JupyterLabJobService, cls._from_rest_job_service_object(obj))
+
+    def _to_rest_object(self) -> RestJobService:
+        return self._to_rest_job_service()
+
+
+class VsCodeJobService(JobServiceBase):
+    """VS Code job service configuration.
+
+    :ivar type: Specifies the type of job service. Set automatically to "vs_code" for this class.
+    :vartype type: str
+    :keyword endpoint: The endpoint URL.
+    :paramtype endpoint: Optional[str]
+    :keyword port: The port for the endpoint.
+    :paramtype port: Optional[int]
+    :keyword nodes: Indicates whether the service has to run in all nodes.
+    :paramtype nodes: Optional[Literal["all"]]
+    :keyword properties: Additional properties to set on the endpoint.
+    :paramtype properties: Optional[dict[str, str]]
+    :keyword status: The status of the endpoint.
+    :paramtype status: Optional[str]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_misc.py
+            :start-after: [START ssh_job_service_configuration]
+            :end-before: [END ssh_job_service_configuration]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a VsCodeJobService configuration on a command job.
+    """
+
+    def __init__(
+        self,
+        *,
+        endpoint: Optional[str] = None,
+        nodes: Optional[Literal["all"]] = None,
+        status: Optional[str] = None,
+        port: Optional[int] = None,
+        properties: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> None:
+        super().__init__(
+            endpoint=endpoint,
+            nodes=nodes,
+            status=status,
+            port=port,
+            properties=properties,
+            **kwargs,
+        )
+        self.type = JobServiceTypeNames.EntityNames.VS_CODE
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestJobService) -> "VsCodeJobService":
+        return cast(VsCodeJobService, cls._from_rest_job_service_object(obj))
+
+    def _to_rest_object(self) -> RestJobService:
+        return self._to_rest_job_service()
+
+
+def _append_or_update_properties(
+    properties: Optional[Dict[str, str]], key: str, value: Optional[str]
+) -> Dict[str, str]:
+    if value and not properties:
+        properties = {key: value}
+
+    if value and properties:
+        properties.update({key: value})
+    return properties if properties is not None else {}
+
+
+def _get_property(properties: Dict[str, str], key: str) -> Optional[str]:
+    return properties.get(key, None) if properties else None
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/__init__.py
new file mode 100644
index 00000000..fdf8caba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/__init__.py
@@ -0,0 +1,5 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/parallel_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/parallel_job.py
new file mode 100644
index 00000000..49b2c992
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/parallel_job.py
@@ -0,0 +1,244 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import logging
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union
+
+from azure.ai.ml._restclient.v2022_02_01_preview.models import JobBaseData
+from azure.ai.ml._schema.job.parallel_job import ParallelJobSchema
+from azure.ai.ml._utils.utils import is_data_binding_expression
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE
+from azure.ai.ml.entities._credentials import (
+    AmlTokenConfiguration,
+    ManagedIdentityConfiguration,
+    UserIdentityConfiguration,
+)
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._util import load_from_dict
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException
+
+from ..job import Job
+from ..job_io_mixin import JobIOMixin
+from .parameterized_parallel import ParameterizedParallel
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._builders import Parallel
+    from azure.ai.ml.entities._component.parallel_component import ParallelComponent
+
+module_logger = logging.getLogger(__name__)
+
+
+class ParallelJob(Job, ParameterizedParallel, JobIOMixin):
+    """Parallel job.
+
+    :param name: Name of the job.
+    :type name: str
+    :param version: Version of the job.
+    :type version: str
+    :param id:  Global id of the resource, Azure Resource Manager ID.
+    :type id: str
+    :param type:  Type of the job, supported is 'parallel'.
+    :type type: str
+    :param description: Description of the job.
+    :type description: str
+    :param tags: Internal use only.
+    :type tags: dict
+    :param properties: Internal use only.
+    :type properties: dict
+    :param display_name: Display name of the job.
+    :type display_name: str
+    :param retry_settings: parallel job run failed retry
+    :type retry_settings: BatchRetrySettings
+    :param logging_level: A string of the logging level name
+    :type logging_level: str
+    :param max_concurrency_per_instance: The max parallellism that each compute instance has.
+    :type max_concurrency_per_instance: int
+    :param error_threshold: The number of item processing failures should be ignored.
+    :type error_threshold: int
+    :param mini_batch_error_threshold: The number of mini batch processing failures should be ignored.
+    :type mini_batch_error_threshold: int
+    :keyword identity: The identity that the job will use while running on compute.
+    :paramtype identity: Optional[Union[~azure.ai.ml.ManagedIdentityConfiguration, ~azure.ai.ml.AmlTokenConfiguration,
+        ~azure.ai.ml.UserIdentityConfiguration]]
+    :param task: The parallel task.
+    :type task: ParallelTask
+    :param mini_batch_size: The mini batch size.
+    :type mini_batch_size: str
+    :param partition_keys: The partition keys.
+    :type partition_keys: list
+    :param input_data: The input data.
+    :type input_data: str
+    :param inputs: Inputs of the job.
+    :type inputs: dict
+    :param outputs: Outputs of the job.
+    :type outputs: dict
+    """
+
+    def __init__(
+        self,
+        *,
+        inputs: Optional[Dict[str, Union[Input, str, bool, int, float]]] = None,
+        outputs: Optional[Dict[str, Output]] = None,
+        identity: Optional[
+            Union[ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration, Dict]
+        ] = None,
+        **kwargs: Any,
+    ):
+        kwargs[TYPE] = JobType.PARALLEL
+
+        super().__init__(**kwargs)
+
+        self.inputs = inputs  # type: ignore[assignment]
+        self.outputs = outputs  # type: ignore[assignment]
+        self.identity = identity
+
+    def _to_dict(self) -> Dict:
+        res: dict = ParallelJobSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def _to_rest_object(self) -> None:
+        pass
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "ParallelJob":
+        loaded_data = load_from_dict(ParallelJobSchema, data, context, additional_message, **kwargs)
+        return ParallelJob(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
+
+    @classmethod
+    def _load_from_rest(cls, obj: JobBaseData) -> None:
+        pass
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "ParallelComponent":
+        """Translate a parallel job to component job.
+
+        :param context: Context of parallel job YAML file.
+        :type context: dict
+        :return: Translated parallel component.
+        :rtype: ParallelComponent
+        """
+        from azure.ai.ml.entities._component.parallel_component import ParallelComponent
+
+        pipeline_job_dict = kwargs.get("pipeline_job_dict", {})
+        context = context or {BASE_PATH_CONTEXT_KEY: Path("./")}
+
+        # Create anonymous parallel component with default version as 1
+        init_kwargs = {}
+        for key in [
+            "mini_batch_size",
+            "partition_keys",
+            "logging_level",
+            "max_concurrency_per_instance",
+            "error_threshold",
+            "mini_batch_error_threshold",
+            "retry_settings",
+            "resources",
+        ]:
+            value = getattr(self, key)
+            from azure.ai.ml.entities import BatchRetrySettings, JobResourceConfiguration
+
+            values_to_check: List = []
+            if key == "retry_settings" and isinstance(value, BatchRetrySettings):
+                values_to_check = [value.max_retries, value.timeout]
+            elif key == "resources" and isinstance(value, JobResourceConfiguration):
+                values_to_check = [
+                    value.locations,
+                    value.instance_count,
+                    value.instance_type,
+                    value.shm_size,
+                    value.max_instance_count,
+                    value.docker_args,
+                ]
+            else:
+                values_to_check = [value]
+
+            # note that component level attributes can not be data binding expressions
+            # so filter out data binding expression properties here;
+            # they will still take effect at node level according to _to_node
+            if any(
+                map(
+                    lambda x: is_data_binding_expression(x, binding_prefix=["parent", "inputs"], is_singular=False)
+                    or is_data_binding_expression(x, binding_prefix=["inputs"], is_singular=False),
+                    values_to_check,
+                )
+            ):
+                continue
+
+            init_kwargs[key] = getattr(self, key)
+
+        return ParallelComponent(
+            base_path=context[BASE_PATH_CONTEXT_KEY],
+            # for parallel_job.task, all attributes for this are string for now so data binding expression is allowed
+            # in SDK level naturally, but not sure if such component is valid. leave the validation to service side.
+            task=self.task,
+            inputs=self._to_inputs(inputs=self.inputs, pipeline_job_dict=pipeline_job_dict),
+            outputs=self._to_outputs(outputs=self.outputs, pipeline_job_dict=pipeline_job_dict),
+            input_data=self.input_data,
+            # keep them if no data binding expression detected to keep the behavior of to_component
+            **init_kwargs,
+        )
+
+    def _to_node(self, context: Optional[Dict] = None, **kwargs: Any) -> "Parallel":
+        """Translate a parallel job to a pipeline node.
+
+        :param context: Context of parallel job YAML file.
+        :type context: dict
+        :return: Translated parallel component.
+        :rtype: Parallel
+        """
+        from azure.ai.ml.entities._builders import Parallel
+
+        component = self._to_component(context, **kwargs)
+
+        return Parallel(
+            component=component,
+            compute=self.compute,
+            # Need to supply the inputs with double curly.
+            inputs=self.inputs,  # type: ignore[arg-type]
+            outputs=self.outputs,  # type: ignore[arg-type]
+            mini_batch_size=self.mini_batch_size,
+            partition_keys=self.partition_keys,
+            input_data=self.input_data,
+            # task will be inherited from component & base_path will be set correctly.
+            retry_settings=self.retry_settings,
+            logging_level=self.logging_level,
+            max_concurrency_per_instance=self.max_concurrency_per_instance,
+            error_threshold=self.error_threshold,
+            mini_batch_error_threshold=self.mini_batch_error_threshold,
+            environment_variables=self.environment_variables,
+            properties=self.properties,
+            identity=self.identity,
+            resources=self.resources if self.resources and not isinstance(self.resources, dict) else None,
+        )
+
+    def _validate(self) -> None:
+        if self.name is None:
+            msg = "Job name is required"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.MISSING_FIELD,
+            )
+        if self.compute is None:
+            msg = "compute is required"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.MISSING_FIELD,
+            )
+        if self.task is None:
+            msg = "task is required"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.MISSING_FIELD,
+            )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/parallel_task.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/parallel_task.py
new file mode 100644
index 00000000..7325aed3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/parallel_task.py
@@ -0,0 +1,119 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from os import PathLike
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+# from azure.ai.ml.entities._deployment.code_configuration import CodeConfiguration
+from azure.ai.ml._schema.component.parallel_task import ComponentParallelTaskSchema
+from azure.ai.ml._utils.utils import load_yaml
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, PARAMS_OVERRIDE_KEY
+from azure.ai.ml.entities._assets.environment import Environment
+from azure.ai.ml.entities._mixins import DictMixin, RestTranslatableMixin
+from azure.ai.ml.entities._util import load_from_dict
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+class ParallelTask(RestTranslatableMixin, DictMixin):
+    """Parallel task.
+
+    :param type: The type of the parallel task.
+        Possible values are 'run_function'and 'model'.
+    :type type: str
+    :param code: A local or remote path pointing at source code.
+    :type code: str
+    :param entry_script: User script which will be run in parallel on multiple nodes. This is
+        specified as a local file path.
+        The entry_script should contain two functions:
+        ``init()``: this function should be used for any costly or common preparation for subsequent inferences,
+        e.g., deserializing and loading the model into a global object.
+        ``run(mini_batch)``: The method to be parallelized. Each invocation will have one mini-batch.
+        'mini_batch': Batch inference will invoke run method and pass either a list or a Pandas DataFrame as an
+        argument to the method. Each entry in min_batch will be a filepath if input is a FileDataset,
+        a Pandas DataFrame if input is a TabularDataset.
+        run() method should return a Pandas DataFrame or an array.
+        For append_row output_action, these returned elements are appended into the common output file.
+        For summary_only, the contents of the elements are ignored. For all output actions,
+        each returned output element indicates one successful inference of input element in the input mini-batch.
+        Each parallel worker process will call `init` once and then loop over `run` function until all mini-batches
+        are processed.
+    :type entry_script: str
+    :param program_arguments: The arguments of the parallel task.
+    :type program_arguments: str
+    :param model: The model of the parallel task.
+    :type model: str
+    :param append_row_to: All values output by run() method invocations will be aggregated into
+        one unique file which is created in the output location.
+        if it is not set, 'summary_only' would invoked,  which means user script is expected to store the output itself.
+    :type append_row_to: str
+    :param environment: Environment that training job will run in.
+    :type environment: Union[Environment, str]
+    """
+
+    def __init__(
+        self,  # pylint: disable=unused-argument
+        *,
+        type: Optional[str] = None,  # pylint: disable=redefined-builtin
+        code: Optional[str] = None,
+        entry_script: Optional[str] = None,
+        program_arguments: Optional[str] = None,
+        model: Optional[str] = None,
+        append_row_to: Optional[str] = None,
+        environment: Optional[Union[Environment, str]] = None,
+        **kwargs: Any,
+    ):
+        self.type = type
+        self.code = code
+        self.entry_script = entry_script
+        self.program_arguments = program_arguments
+        self.model = model
+        self.append_row_to = append_row_to
+        self.environment: Any = environment
+
+    def _to_dict(self) -> Dict:
+        # pylint: disable=no-member
+        res: dict = ComponentParallelTaskSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    @classmethod
+    def _load(
+        cls,  # pylint: disable=unused-argument
+        path: Optional[Union[PathLike, str]] = None,
+        params_override: Optional[list] = None,
+        **kwargs: Any,
+    ) -> "ParallelTask":
+        params_override = params_override or []
+        data = load_yaml(path)
+        return ParallelTask._load_from_dict(data=data, path=path, params_override=params_override)
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: dict,
+        path: Optional[Union[PathLike, str]] = None,
+        params_override: Optional[list] = None,
+        **kwargs: Any,
+    ) -> "ParallelTask":
+        params_override = params_override or []
+        context = {
+            BASE_PATH_CONTEXT_KEY: Path(path).parent if path else Path.cwd(),
+            PARAMS_OVERRIDE_KEY: params_override,
+        }
+        res: ParallelTask = load_from_dict(ComponentParallelTaskSchema, data, context, **kwargs)
+        return res
+
+    @classmethod
+    def _from_dict(cls, dct: dict) -> "ParallelTask":
+        obj = cls(**dict(dct.items()))
+        return obj
+
+    def _validate(self) -> None:
+        if self.type is None:
+            msg = "'type' is required for ParallelTask {}."
+            raise ValidationException(
+                message=msg.format(self.type),
+                target=ErrorTarget.COMPONENT,
+                no_personal_data_message=msg.format(""),
+                error_category=ErrorCategory.USER_ERROR,
+            )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/parameterized_parallel.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/parameterized_parallel.py
new file mode 100644
index 00000000..6b5dbced
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/parameterized_parallel.py
@@ -0,0 +1,96 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import logging
+from typing import Any, Dict, List, Optional, Union
+
+from ..job_resource_configuration import JobResourceConfiguration
+from .parallel_task import ParallelTask
+from .retry_settings import RetrySettings
+
+module_logger = logging.getLogger(__name__)
+
+
+class ParameterizedParallel:
+    """Parallel component that contains the traning parallel and supporting parameters for the parallel.
+
+    :param retry_settings: parallel component run failed retry
+    :type retry_settings: BatchRetrySettings
+    :param logging_level: A string of the logging level name
+    :type logging_level: str
+    :param max_concurrency_per_instance: The max parallellism that each compute instance has.
+    :type max_concurrency_per_instance: int
+    :param error_threshold: The number of item processing failures should be ignored.
+    :type error_threshold: int
+    :param mini_batch_error_threshold: The number of mini batch processing failures should be ignored.
+    :type mini_batch_error_threshold: int
+    :param task: The parallel task.
+    :type task: ParallelTask
+    :param mini_batch_size: The mini batch size.
+    :type mini_batch_size: str
+    :param input_data: The input data.
+    :type input_data: str
+    :param resources: Compute Resource configuration for the job.
+    :type resources: Union[Dict, ~azure.ai.ml.entities.JobResourceConfiguration]
+    """
+
+    # pylint: disable=too-many-instance-attributes
+    def __init__(
+        self,
+        retry_settings: Optional[RetrySettings] = None,
+        logging_level: Optional[str] = None,
+        max_concurrency_per_instance: Optional[int] = None,
+        error_threshold: Optional[int] = None,
+        mini_batch_error_threshold: Optional[int] = None,
+        input_data: Optional[str] = None,
+        task: Optional[ParallelTask] = None,
+        mini_batch_size: Optional[int] = None,
+        partition_keys: Optional[List] = None,
+        resources: Optional[Union[dict, JobResourceConfiguration]] = None,
+        environment_variables: Optional[Dict] = None,
+    ):
+        self.mini_batch_size = mini_batch_size
+        self.partition_keys = partition_keys
+        self.task = task
+        self.retry_settings = retry_settings
+        self.input_data = input_data
+        self.logging_level = logging_level
+        self.max_concurrency_per_instance = max_concurrency_per_instance
+        self.error_threshold = error_threshold
+        self.mini_batch_error_threshold = mini_batch_error_threshold
+        self.resources = resources
+        self.environment_variables = dict(environment_variables) if environment_variables else {}
+
+    @property
+    def task(self) -> Optional[ParallelTask]:
+        res: Optional[ParallelTask] = self._task
+        return res
+
+    @task.setter
+    def task(self, value: Any) -> None:
+        if isinstance(value, dict):
+            value = ParallelTask(**value)
+        self._task = value
+
+    @property
+    def resources(self) -> Optional[Union[dict, JobResourceConfiguration]]:
+        res: Optional[Union[dict, JobResourceConfiguration]] = self._resources
+        return res
+
+    @resources.setter
+    def resources(self, value: Any) -> None:
+        if isinstance(value, dict):
+            value = JobResourceConfiguration(**value)
+        self._resources = value
+
+    @property
+    def retry_settings(self) -> Optional[RetrySettings]:
+        res: Optional[RetrySettings] = self._retry_settings
+        return res
+
+    @retry_settings.setter
+    def retry_settings(self, value: Any) -> None:
+        if isinstance(value, dict):
+            value = RetrySettings(**value)
+        self._retry_settings = value
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/retry_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/retry_settings.py
new file mode 100644
index 00000000..2fb19ba1
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/retry_settings.py
@@ -0,0 +1,78 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from os import PathLike
+from pathlib import Path
+from typing import Any, Dict, Optional, Union
+
+from azure.ai.ml._schema.component.retry_settings import RetrySettingsSchema
+from azure.ai.ml._utils.utils import load_yaml
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, PARAMS_OVERRIDE_KEY
+from azure.ai.ml.entities._mixins import DictMixin, RestTranslatableMixin
+from azure.ai.ml.entities._util import load_from_dict
+
+
+class RetrySettings(RestTranslatableMixin, DictMixin):
+    """Parallel RetrySettings.
+
+    :param timeout: Timeout in seconds for each invocation of the run() method.
+        (optional) This value could be set through PipelineParameter.
+    :type timeout: int
+    :param max_retries: The number of maximum tries for a failed or timeout mini batch.
+        The range is [1, int.max]. This value could be set through PipelineParameter.
+        A mini batch with dequeue count greater than this won't be processed again and will be deleted directly.
+    :type max_retries: int
+    """
+
+    def __init__(
+        self,  # pylint: disable=unused-argument
+        *,
+        timeout: Optional[Union[int, str]] = None,
+        max_retries: Optional[Union[int, str]] = None,
+        **kwargs: Any,
+    ):
+        self.timeout = timeout
+        self.max_retries = max_retries
+
+    def _to_dict(self) -> Dict:
+        res: dict = RetrySettingsSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)  # pylint: disable=no-member
+        return res
+
+    @classmethod
+    def _load(
+        cls,  # pylint: disable=unused-argument
+        path: Optional[Union[PathLike, str]] = None,
+        params_override: Optional[list] = None,
+        **kwargs: Any,
+    ) -> "RetrySettings":
+        params_override = params_override or []
+        data = load_yaml(path)
+        return RetrySettings._load_from_dict(data=data, path=path, params_override=params_override)
+
+    @classmethod
+    def _load_from_dict(
+        cls,
+        data: dict,
+        path: Optional[Union[PathLike, str]] = None,
+        params_override: Optional[list] = None,
+        **kwargs: Any,
+    ) -> "RetrySettings":
+        params_override = params_override or []
+        context = {
+            BASE_PATH_CONTEXT_KEY: Path(path).parent if path else Path.cwd(),
+            PARAMS_OVERRIDE_KEY: params_override,
+        }
+        res: RetrySettings = load_from_dict(RetrySettingsSchema, data, context, **kwargs)
+        return res
+
+    @classmethod
+    def _from_dict(cls, dct: dict) -> "RetrySettings":
+        obj = cls(**dict(dct.items()))
+        return obj
+
+    def _to_rest_object(self) -> Dict:
+        return self._to_dict()
+
+    @classmethod
+    def _from_rest_object(cls, obj: dict) -> "RetrySettings":
+        return cls._from_dict(obj)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/run_function.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/run_function.py
new file mode 100644
index 00000000..180cee76
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parallel/run_function.py
@@ -0,0 +1,66 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+
+from typing import Any, Optional, Union
+
+from azure.ai.ml.constants import ParallelTaskType
+from azure.ai.ml.entities._assets.environment import Environment
+
+from .parallel_task import ParallelTask
+
+
+class RunFunction(ParallelTask):
+    """Run Function.
+
+    :param code: A local or remote path pointing at source code.
+    :type code: str
+    :param entry_script: User script which will be run in parallel on multiple nodes. This is
+        specified as a local file path.
+        The entry_script should contain two functions:
+        ``init()``: this function should be used for any costly or common preparation for subsequent inferences,
+        e.g., deserializing and loading the model into a global object.
+        ``run(mini_batch)``: The method to be parallelized. Each invocation will have one mini-batch.
+        'mini_batch': Batch inference will invoke run method and pass either a list or a Pandas DataFrame as an
+        argument to the method. Each entry in min_batch will be a filepath if input is a FileDataset,
+        a Pandas DataFrame if input is a TabularDataset.
+        run() method should return a Pandas DataFrame or an array.
+        For append_row output_action, these returned elements are appended into the common output file.
+        For summary_only, the contents of the elements are ignored. For all output actions,
+        each returned output element indicates one successful inference of input element in the input mini-batch.
+        Each parallel worker process will call `init` once and then loop over `run` function until all mini-batches
+        are processed.
+    :type entry_script: str
+    :param program_arguments: The arguments of the parallel task.
+    :type args: str
+    :param model: The model of the parallel task.
+    :type model: str
+    :param append_row_to: All values output by run() method invocations will be aggregated into
+        one unique file which is created in the output location.
+        if it is not set, 'summary_only' would invoked,  which means user script is expected to store the output itself.
+    :type append_row_to: str
+    :param environment: Environment that training job will run in.
+    :type environment: Union[Environment, str]
+    """
+
+    def __init__(
+        self,
+        *,
+        code: Optional[str] = None,
+        entry_script: Optional[str] = None,
+        program_arguments: Optional[str] = None,
+        model: Optional[str] = None,
+        append_row_to: Optional[str] = None,
+        environment: Optional[Union[Environment, str]] = None,
+        **kwargs: Any,
+    ):
+        super().__init__(
+            code=code,
+            entry_script=entry_script,
+            program_arguments=program_arguments,
+            model=model,
+            append_row_to=append_row_to,
+            environment=environment,
+            type=ParallelTaskType.RUN_FUNCTION,
+        )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parameterized_command.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parameterized_command.py
new file mode 100644
index 00000000..57604b38
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parameterized_command.py
@@ -0,0 +1,170 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import logging
+import os
+from typing import Dict, Optional, Union
+
+from marshmallow import INCLUDE
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import SweepJob
+from azure.ai.ml._schema.core.fields import ExperimentalField
+from azure.ai.ml.entities._assets import Environment
+
+from ..._schema import NestedField, UnionField
+from ..._schema.job.distribution import (
+    MPIDistributionSchema,
+    PyTorchDistributionSchema,
+    RayDistributionSchema,
+    TensorFlowDistributionSchema,
+)
+from .distribution import (
+    DistributionConfiguration,
+    MpiDistribution,
+    PyTorchDistribution,
+    RayDistribution,
+    TensorFlowDistribution,
+)
+from .job_resource_configuration import JobResourceConfiguration
+from .queue_settings import QueueSettings
+
+module_logger = logging.getLogger(__name__)
+
+# no reference found. leave it for future use.
+INPUT_BINDING_PREFIX = "AZURE_ML_INPUT_"
+OLD_INPUT_BINDING_PREFIX = "AZURE_ML_INPUT"
+
+
+class ParameterizedCommand:
+    """Command component version that contains the command and supporting parameters for a Command component
+    or job.
+
+    This class should not be instantiated directly. Instead, use the child class
+    ~azure.ai.ml.entities.CommandComponent.
+
+    :param command: The command to be executed. Defaults to "".
+    :type command: str
+    :param resources: The compute resource configuration for the command.
+    :type resources: Optional[Union[dict, ~azure.ai.ml.entities.JobResourceConfiguration]]
+    :param code: The source code to run the job. Can be a local path or "http:", "https:", or "azureml:" url pointing
+        to a remote location.
+    :type code: Optional[str]
+    :param environment_variables: A dictionary of environment variable names and values.
+        These environment variables are set on the process where user script is being executed.
+    :type environment_variables: Optional[dict[str, str]]
+    :param distribution: The distribution configuration for distributed jobs.
+    :type distribution: Optional[Union[dict, ~azure.ai.ml.PyTorchDistribution, ~azure.ai.ml.MpiDistribution,
+        ~azure.ai.ml.TensorFlowDistribution, ~azure.ai.ml.RayDistribution]]
+    :param environment: The environment that the job will run in.
+    :type environment: Optional[Union[str, ~azure.ai.ml.entities.Environment]]
+    :param queue_settings: The queue settings for the job.
+    :type queue_settings: Optional[~azure.ai.ml.entities.QueueSettings]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+    """
+
+    def __init__(
+        self,
+        command: Optional[str] = "",
+        resources: Optional[Union[dict, JobResourceConfiguration]] = None,
+        code: Optional[Union[str, os.PathLike]] = None,
+        environment_variables: Optional[Dict] = None,
+        distribution: Optional[
+            Union[
+                Dict,
+                MpiDistribution,
+                TensorFlowDistribution,
+                PyTorchDistribution,
+                RayDistribution,
+                DistributionConfiguration,
+            ]
+        ] = None,
+        environment: Optional[Union[Environment, str]] = None,
+        queue_settings: Optional[QueueSettings] = None,
+        **kwargs: Dict,
+    ) -> None:
+        super().__init__(**kwargs)
+        self.command = command
+        self.code = code
+        self.environment_variables = dict(environment_variables) if environment_variables else {}
+        self.environment = environment
+        self.distribution = distribution
+        self.resources = resources  # type: ignore[assignment]
+        self.queue_settings = queue_settings
+
+    @property
+    def distribution(
+        self,
+    ) -> Optional[
+        Union[
+            dict,
+            MpiDistribution,
+            TensorFlowDistribution,
+            PyTorchDistribution,
+            RayDistribution,
+            DistributionConfiguration,
+        ]
+    ]:
+        """The configuration for the distributed command component or job.
+
+        :return: The distribution configuration.
+        :rtype: Union[~azure.ai.ml.PyTorchDistribution, ~azure.ai.ml.MpiDistribution,
+            ~azure.ai.ml.TensorFlowDistribution, ~azure.ai.ml.RayDistribution]
+        """
+        return self._distribution
+
+    @distribution.setter
+    def distribution(self, value: Union[dict, PyTorchDistribution, MpiDistribution]) -> None:
+        """Sets the configuration for the distributed command component or job.
+
+        :param value: The distribution configuration for distributed jobs.
+        :type value: Union[dict, ~azure.ai.ml.PyTorchDistribution, ~azure.ai.ml.MpiDistribution,
+            ~azure.ai.ml.TensorFlowDistribution, ~azure.ai.ml.RayDistribution]
+        """
+        if isinstance(value, dict):
+            dist_schema = UnionField(
+                [
+                    NestedField(PyTorchDistributionSchema, unknown=INCLUDE),
+                    NestedField(TensorFlowDistributionSchema, unknown=INCLUDE),
+                    NestedField(MPIDistributionSchema, unknown=INCLUDE),
+                    ExperimentalField(NestedField(RayDistributionSchema, unknown=INCLUDE)),
+                ]
+            )
+            value = dist_schema._deserialize(value=value, attr=None, data=None)
+        self._distribution = value
+
+    @property
+    def resources(self) -> JobResourceConfiguration:
+        """The compute resource configuration for the command component or job.
+
+        :return: The compute resource configuration for the command component or job.
+        :rtype: ~azure.ai.ml.entities.JobResourceConfiguration
+        """
+        return self._resources
+
+    @resources.setter
+    def resources(self, value: Union[dict, JobResourceConfiguration]) -> None:
+        """Sets the compute resource configuration for the command component or job.
+
+        :param value: The compute resource configuration for the command component or job.
+        :type value: Union[dict, ~azure.ai.ml.entities.JobResourceConfiguration]
+        """
+        if isinstance(value, dict):
+            value = JobResourceConfiguration(**value)
+        self._resources = value
+
+    @classmethod
+    def _load_from_sweep_job(cls, sweep_job: SweepJob) -> "ParameterizedCommand":
+        parameterized_command = cls(
+            command=sweep_job.trial.command,
+            code=sweep_job.trial.code_id,
+            environment_variables=sweep_job.trial.environment_variables,
+            environment=sweep_job.trial.environment_id,
+            distribution=DistributionConfiguration._from_rest_object(sweep_job.trial.distribution),
+            resources=JobResourceConfiguration._from_rest_object(sweep_job.trial.resources),
+            queue_settings=QueueSettings._from_rest_object(sweep_job.queue_settings),
+        )
+        return parameterized_command
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parameterized_spark.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parameterized_spark.py
new file mode 100644
index 00000000..c8a9a0c0
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/parameterized_spark.py
@@ -0,0 +1,88 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+import os
+from typing import Any, Dict, List, Optional, Union
+
+from azure.ai.ml.entities._assets import Environment
+from azure.ai.ml.entities._job.spark_job_entry import SparkJobEntry
+
+from .._job.spark_job_entry_mixin import SparkJobEntryMixin
+
+DUMMY_IMAGE = "conda/miniconda3"
+
+
+class ParameterizedSpark(SparkJobEntryMixin):
+    """
+    This class should not be instantiated directly. Instead, use the child class ~azure.ai.ml.entities.SparkComponent.
+
+    Spark component that contains supporting parameters.
+
+    :param code: The source code to run the job. Can be a local path or "http:", "https:", or "azureml:" url pointing
+        to a remote location.
+    :type code: Optional[Union[str, os.PathLike]]
+    :param entry: The file or class entry point.
+    :type entry: dict[str, str]
+    :param py_files: The list of .zip, .egg or .py files to place on the PYTHONPATH for Python apps.
+    :type py_files: Optional[list[str]]
+    :param jars: The list of .JAR files to include on the driver and executor classpaths.
+    :type jars: Optional[list[str]]
+    :param files: The list of files to be placed in the working directory of each executor.
+    :type files: Optional[list[str]]
+    :param archives: The list of archives to be extracted into the working directory of each executor.
+    :type archives: Optional[list[str]]
+    :param conf: A dictionary with pre-defined Spark configurations key and values.
+    :type conf: Optional[dict[str, str]]
+    :param environment: The Azure ML environment to run the job in.
+    :type environment: Optional[Union[str, ~azure.ai.ml.entities.Environment]]
+    :param args: The arguments for the job.
+    :type args: Optional[str]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+    """
+
+    def __init__(
+        self,
+        code: Optional[Union[str, os.PathLike]] = ".",
+        entry: Optional[Union[Dict[str, str], SparkJobEntry]] = None,
+        py_files: Optional[List[str]] = None,
+        jars: Optional[List[str]] = None,
+        files: Optional[List[str]] = None,
+        archives: Optional[List[str]] = None,
+        conf: Optional[Dict[str, str]] = None,
+        environment: Optional[Union[str, Environment]] = None,
+        args: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        self.args = None
+
+        super().__init__(**kwargs)
+        self.code = code
+        self.entry = entry
+        self.py_files = py_files
+        self.jars = jars
+        self.files = files
+        self.archives = archives
+        self.conf = conf
+        self.environment = environment
+        self.args = args
+
+    @property
+    def environment(self) -> Optional[Union[str, Environment]]:
+        """The Azure ML environment to run the Spark component or job in.
+
+        :return: The Azure ML environment to run the Spark component or job in.
+        :rtype: Optional[Union[str, ~azure.ai.ml.entities.Environment]]
+        """
+        if isinstance(self._environment, Environment) and self._environment.image is None:
+            return Environment(conda_file=self._environment.conda_file, image=DUMMY_IMAGE)
+        return self._environment
+
+    @environment.setter
+    def environment(self, value: Optional[Union[str, Environment]]) -> None:
+        """Sets the Azure ML environment to run the Spark component or job in.
+
+        :param value: The Azure ML environment to run the Spark component or job in.
+        :type value: Optional[Union[str, ~azure.ai.ml.entities.Environment]]
+        """
+        self._environment = value
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/__init__.py
new file mode 100644
index 00000000..fdf8caba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/__init__.py
@@ -0,0 +1,5 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_attr_dict.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_attr_dict.py
new file mode 100644
index 00000000..cf8d92be
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_attr_dict.py
@@ -0,0 +1,161 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import logging
+from abc import ABC
+from typing import Any, Dict, Generic, List, Optional, TypeVar
+
+K = TypeVar("K")
+V = TypeVar("V")
+
+
+class _AttrDict(Generic[K, V], Dict, ABC):
+    """This class is used for accessing values with instance.some_key. It supports the following scenarios:
+
+    1. Setting arbitrary attribute, eg: obj.resource_layout.node_count = 2
+      1.1 Setting same nested filed twice will return same object, eg:
+              obj.resource_layout.node_count = 2
+              obj.resource_layout.process_count_per_node = 2
+              obj.resource_layout will be {"node_count": 2, "process_count_per_node": 2}
+      1.2 Only public attribute is supported, eg: obj._resource_layout._node_count = 2 will raise AttributeError
+      1.3 All set attribute can be recorded, eg:
+              obj.target = "aml"
+              obj.resource_layout.process_count_per_node = 2
+              obj.get_attr() will return {"target": "aml", "resource_layout": {"process_count_per_node": 2}}
+    2. Getting arbitrary attribute, getting non-exist attribute will return an empty dict.
+    3. Calling arbitrary methods is not allowed, eg: obj.resource_layout() should raise AttributeError
+    """
+
+    def __init__(self, allowed_keys: Optional[Dict] = None, **kwargs: Any):
+        """Initialize a attribute dictionary.
+
+        :param allowed_keys: A dictionary of keys that allowed to set as arbitrary attributes. None means all keys can
+            be set as arbitrary attributes.
+
+        :type dict
+        :param kwargs: A dictionary of additional configuration parameters.
+        :type kwargs: dict
+        """
+        super(_AttrDict, self).__init__(**kwargs)
+        if allowed_keys is None:
+            # None allowed_keys means no restriction on keys can be set for _AttrDict
+            self._allowed_keys = {}
+            self._key_restriction = False
+        else:
+            # Otherwise use allowed_keys to restrict keys can be set for _AttrDict
+            self._allowed_keys = dict(allowed_keys)
+            self._key_restriction = True
+        self._logger = logging.getLogger("attr_dict")
+
+    def _initializing(self) -> bool:
+        # use this to indicate ongoing init process, sub class need to make sure this return True during init process.
+        return False
+
+    def _get_attrs(self) -> dict:
+        """Get all arbitrary attributes which has been set, empty values are excluded.
+
+        :return: A dict which contains all arbitrary attributes set by user.
+        :rtype: dict
+        """
+
+        # TODO: check this
+        def remove_empty_values(data: Dict) -> Dict:
+            if not isinstance(data, dict):
+                return data
+            # skip empty dicts as default value of _AttrDict is empty dict
+            return {k: remove_empty_values(v) for k, v in data.items() if v or not isinstance(v, dict)}
+
+        return remove_empty_values(self)
+
+    def _is_arbitrary_attr(self, attr_name: str) -> bool:
+        """Checks if a given attribute name should be treat as arbitrary attribute.
+
+        Attributes inside _AttrDict can be non-arbitrary attribute or arbitrary attribute.
+        Non-arbitrary attributes are normal attributes like other object which stores in self.__dict__.
+        Arbitrary attributes are attributes stored in the dictionary it self, what makes it special it it's value
+        can be an instance of _AttrDict
+        Take `obj = _AttrDict(allowed_keys={"resource_layout": {"node_count": None}})` as an example.
+        `obj.some_key` is accessing non-arbitrary attribute.
+        `obj.resource_layout` is accessing arbitrary attribute, user can use `obj.resource_layout.node_count = 1` to
+        assign value to it.
+
+        :param attr_name: Attribute name
+        :type attr_name: str
+        :return: If the given attribute name should be treated as arbitrary attribute.
+        :rtype: bool
+        """
+        # Internal attribute won't be set as arbitrary attribute.
+        if attr_name.startswith("_"):
+            return False
+        # All attributes set in __init__ won't be set as arbitrary attribute
+        if self._initializing():
+            return False
+        # If there's key restriction, only keys in it can be set as arbitrary attribute.
+        if self._key_restriction and attr_name not in self._allowed_keys:
+            return False
+        # Attributes already in attribute dict will not be set as arbitrary attribute.
+        try:
+            self.__getattribute__(attr_name)
+        except AttributeError:
+            return True
+        return False
+
+    def __getattr__(self, key: Any) -> Any:
+        if not self._is_arbitrary_attr(key):
+            return super().__getattribute__(key)
+        self._logger.debug("getting %s", key)
+        try:
+            return super().__getitem__(key)
+        except KeyError:
+            allowed_keys = self._allowed_keys.get(key, None) if self._key_restriction else None
+            result: Any = _AttrDict(allowed_keys=allowed_keys)
+            self.__setattr__(key, result)
+            return result
+
+    def __setattr__(self, key: Any, value: V) -> None:
+        if not self._is_arbitrary_attr(key):
+            super().__setattr__(key, value)
+        else:
+            self._logger.debug("setting %s to %s", key, value)
+            super().__setitem__(key, value)
+
+    def __setitem__(self, key: Any, value: V) -> None:
+        self.__setattr__(key, value)
+
+    def __getitem__(self, item: V) -> Any:
+        # support attr_dict[item] since dumping it in marshmallow requires this.
+        return self.__getattr__(item)
+
+    def __dir__(self) -> List:
+        # For Jupyter Notebook auto-completion
+        return list(super().__dir__()) + list(self.keys())
+
+
+def has_attr_safe(obj: Any, attr: Any) -> bool:
+    if isinstance(obj, _AttrDict):
+        has_attr = not obj._is_arbitrary_attr(attr)
+    elif isinstance(obj, dict):
+        return attr in obj
+    else:
+        has_attr = hasattr(obj, attr)
+    return has_attr
+
+
+def try_get_non_arbitrary_attr(obj: Any, attr: str) -> Optional[Any]:
+    """Try to get non-arbitrary attribute for potential attribute dict.
+
+    Will not create target attribute if it is an arbitrary attribute in _AttrDict.
+
+    :param obj: The obj
+    :type obj: Any
+    :param attr: The attribute name
+    :type attr: str
+    :return: obj.attr
+    :rtype: Any
+    """
+    if has_attr_safe(obj, attr):
+        return obj[attr] if isinstance(obj, dict) else getattr(obj, attr)
+    return None
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_component_translatable.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_component_translatable.py
new file mode 100644
index 00000000..22be939d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_component_translatable.py
@@ -0,0 +1,412 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+# pylint: disable=protected-access, redefined-builtin
+# disable redefined-builtin to use input as argument name
+import re
+from typing import TYPE_CHECKING, Any, Dict, Optional, Tuple, Union
+
+from pydash import get
+
+from azure.ai.ml._utils.utils import is_data_binding_expression
+from azure.ai.ml.constants._common import AssetTypes
+from azure.ai.ml.constants._component import ComponentJobConstants
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._job.pipeline._io import PipelineInput, PipelineOutput
+from azure.ai.ml.entities._job.sweep.search_space import Choice, Randint, SweepDistribution
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, JobException
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._builders import BaseNode
+    from azure.ai.ml.entities._component.component import Component
+
+
+class ComponentTranslatableMixin:
+    _PYTHON_SDK_TYPE_MAPPING = {
+        float: "number",
+        int: "integer",
+        bool: "boolean",
+        str: "string",
+    }
+
+    @classmethod
+    def _find_source_from_parent_inputs(cls, input: str, pipeline_job_inputs: dict) -> Tuple[str, Optional[str]]:
+        """Find source type and mode of input/output from parent input.
+
+        :param input: The input name
+        :type input: str
+        :param pipeline_job_inputs: The pipeline job inputs
+        :type pipeline_job_inputs: dict
+        :return: A 2-tuple of the type and the mode
+        :rtype: Tuple[str, Optional[str]]
+        """
+        _input_name = input.split(".")[2][:-2]
+        if _input_name not in pipeline_job_inputs.keys():
+            msg = "Failed to find top level definition for input binding {}."
+            raise JobException(
+                message=msg.format(input),
+                no_personal_data_message=msg.format("[input]"),
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        input_data = pipeline_job_inputs[_input_name]
+        input_type = type(input_data)
+        if input_type in cls._PYTHON_SDK_TYPE_MAPPING:
+            return cls._PYTHON_SDK_TYPE_MAPPING[input_type], None
+        return getattr(input_data, "type", AssetTypes.URI_FOLDER), getattr(input_data, "mode", None)
+
+    @classmethod
+    def _find_source_from_parent_outputs(cls, input: str, pipeline_job_outputs: dict) -> Tuple[str, Optional[str]]:
+        """Find source type and mode of input/output from parent output.
+
+        :param input: The input name
+        :type input: str
+        :param pipeline_job_outputs: The pipeline job outputs
+        :type pipeline_job_outputs: dict
+        :return: A 2-tuple of the type and the mode
+        :rtype: Tuple[str, Optional[str]]
+        """
+        _output_name = input.split(".")[2][:-2]
+        if _output_name not in pipeline_job_outputs.keys():
+            msg = "Failed to find top level definition for output binding {}."
+            raise JobException(
+                message=msg.format(input),
+                no_personal_data_message=msg.format("[input]"),
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        output_data = pipeline_job_outputs[_output_name]
+        output_type = type(output_data)
+        if output_type in cls._PYTHON_SDK_TYPE_MAPPING:
+            return cls._PYTHON_SDK_TYPE_MAPPING[output_type], None
+        if isinstance(output_data, dict):
+            if "type" in output_data:
+                output_data_type = output_data["type"]
+            else:
+                output_data_type = AssetTypes.URI_FOLDER
+            if "mode" in output_data:
+                output_data_mode = output_data["mode"]
+            else:
+                output_data_mode = None
+            return output_data_type, output_data_mode
+        return getattr(output_data, "type", AssetTypes.URI_FOLDER), getattr(output_data, "mode", None)
+
+    @classmethod
+    def _find_source_from_other_jobs(
+        cls, input: str, jobs_dict: dict, pipeline_job_dict: dict
+    ) -> Tuple[str, Optional[str]]:
+        """Find source type and mode of input/output from other job.
+
+        :param input: The input name
+        :type input: str
+        :param jobs_dict: The job dict
+        :type jobs_dict:
+        :param pipeline_job_dict: The pipeline job dict
+        :type pipeline_job_dict: dict
+        :return: A 2-tuple of the type and the mode
+        :rtype: Tuple[str, Optional[str]]
+        """
+        from azure.ai.ml.entities import CommandJob
+        from azure.ai.ml.entities._builders import BaseNode
+        from azure.ai.ml.entities._job.automl.automl_job import AutoMLJob
+        from azure.ai.ml.parallel import ParallelJob
+
+        _input_regex = r"\${{parent.jobs.([^.]+).([^.]+).([^.]+)}}"
+        m = re.match(_input_regex, input)
+        if m is None:
+            msg = "Failed to find top level definition for job binding {}."
+            raise JobException(
+                message=msg.format(input),
+                no_personal_data_message=msg.format("[input]"),
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        _input_job_name, _io_type, _name = m.groups()
+        _input_job = jobs_dict[_input_job_name]
+
+        # we only support input of one job is from output of another output, but input mode should be decoupled with
+        # output mode, so we always return None source_mode
+        source_mode = None
+        if isinstance(_input_job, BaseNode):
+            # If source is base node, get type from io builder
+            _source = _input_job[_io_type][_name]
+            try:
+                source_type = _source.type
+                # Todo: get component type for registered component, and no need following codes
+                # source_type is None means _input_job's component is registered component which results in its
+                # input/output type is None.
+                if source_type is None:
+                    if _source._data is None:
+                        # return default type if _input_job's output data is None
+                        source_type = AssetTypes.URI_FOLDER
+                    elif isinstance(_source._data, Output):
+                        # if _input_job data is a Output object and we return its type.
+                        source_type = _source._data.type
+                    else:
+                        #  otherwise _input_job's input/output is bound to pipeline input/output, we continue
+                        #  infer the type according to _source._data. Will return corresponding pipeline
+                        #  input/output type because we didn't get the component.
+                        source_type, _ = cls._find_source_input_output_type(_source._data, pipeline_job_dict)
+                return source_type, source_mode
+            except AttributeError as e:
+                msg = "Failed to get referenced component type {}."
+                raise JobException(
+                    message=msg.format(_input_regex),
+                    no_personal_data_message=msg.format("[_input_regex]"),
+                    target=ErrorTarget.PIPELINE,
+                    error_category=ErrorCategory.USER_ERROR,
+                ) from e
+        if isinstance(_input_job, (CommandJob, ParallelJob)):
+            # If source has not parsed to Command yet, infer type
+            _source = get(_input_job, f"{_io_type}.{_name}")
+            if isinstance(_source, str):
+                source_type, _ = cls._find_source_input_output_type(_source, pipeline_job_dict)
+                return source_type, source_mode
+            return getattr(_source, "type", AssetTypes.URI_FOLDER), source_mode
+        if isinstance(_input_job, AutoMLJob):
+            # If source is AutoMLJob, only outputs is supported
+            if _io_type != "outputs":
+                msg = f"Only binding to AutoMLJob output is supported, currently got {_io_type}"
+                raise JobException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.PIPELINE,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            # AutoMLJob's output type can only be MLTABLE
+            return AssetTypes.MLTABLE, source_mode
+        msg = f"Unknown referenced source job type: {type(_input_job)}."
+        raise JobException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.PIPELINE,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+    @classmethod
+    def _find_source_input_output_type(cls, input: str, pipeline_job_dict: dict) -> Tuple[str, Optional[str]]:
+        """Find source type and mode of input/output.
+
+        :param input: The input binding
+        :type input: str
+        :param pipeline_job_dict: The pipeline job dict
+        :type pipeline_job_dict: dict
+        :return: A 2-tuple of the type and the mode
+        :rtype: Tuple[str, Optional[str]]
+        """
+        pipeline_job_inputs = pipeline_job_dict.get("inputs", {})
+        pipeline_job_outputs = pipeline_job_dict.get("outputs", {})
+        jobs_dict = pipeline_job_dict.get("jobs", {})
+        if is_data_binding_expression(input, ["parent", "inputs"]):
+            return cls._find_source_from_parent_inputs(input, pipeline_job_inputs)
+        if is_data_binding_expression(input, ["parent", "outputs"]):
+            return cls._find_source_from_parent_outputs(input, pipeline_job_outputs)
+        if is_data_binding_expression(input, ["parent", "jobs"]):
+            try:
+                return cls._find_source_from_other_jobs(input, jobs_dict, pipeline_job_dict)
+            except JobException as e:
+                raise e
+            except Exception as e:
+                msg = "Failed to find referenced source for input binding {}"
+                raise JobException(
+                    message=msg.format(input),
+                    no_personal_data_message=msg.format("[input]"),
+                    target=ErrorTarget.PIPELINE,
+                    error_category=ErrorCategory.SYSTEM_ERROR,
+                ) from e
+        else:
+            msg = "Job input in a pipeline can bind only to a job output or a pipeline input"
+            raise JobException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    @classmethod
+    def _to_input(
+        cls,  # pylint: disable=unused-argument
+        input: Union[Input, str, bool, int, float],
+        pipeline_job_dict: Optional[dict] = None,
+        **kwargs: Any,
+    ) -> Input:
+        """Convert a single job input value to component input.
+
+        :param input: The input
+        :type input: Union[Input, str, bool, int, float]
+        :param pipeline_job_dict: The pipeline job dict
+        :type pipeline_job_dict: Optional[dict]
+        :return: The Component Input
+        :rtype: Input
+        """
+        pipeline_job_dict = pipeline_job_dict or {}
+        input_variable: Dict = {}
+
+        if isinstance(input, str) and bool(re.search(ComponentJobConstants.INPUT_PATTERN, input)):
+            # handle input bindings
+            input_variable["type"], input_variable["mode"] = cls._find_source_input_output_type(
+                input, pipeline_job_dict
+            )
+
+        elif isinstance(input, Input):
+            input_variable = input._to_dict()
+        elif isinstance(input, SweepDistribution):
+            if isinstance(input, Choice):
+                if input.values is not None:
+                    input_variable["type"] = cls._PYTHON_SDK_TYPE_MAPPING[type(input.values[0])]
+            elif isinstance(input, Randint):
+                input_variable["type"] = cls._PYTHON_SDK_TYPE_MAPPING[int]
+            else:
+                input_variable["type"] = cls._PYTHON_SDK_TYPE_MAPPING[float]
+
+            input_variable["optional"] = False
+        elif type(input) in cls._PYTHON_SDK_TYPE_MAPPING:
+            input_variable["type"] = cls._PYTHON_SDK_TYPE_MAPPING[type(input)]
+            input_variable["default"] = input
+        elif isinstance(input, PipelineInput):
+            # Infer input type from input data
+            input_variable = input._to_input()._to_dict()
+        else:
+            msg = "'{}' is not supported as component input, supported types are '{}'.".format(
+                type(input), cls._PYTHON_SDK_TYPE_MAPPING.keys()
+            )
+            raise JobException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        return Input(**input_variable)
+
+    @classmethod
+    def _to_input_builder_function(cls, input: Union[Dict, SweepDistribution, Input, str, bool, int, float]) -> Input:
+        input_variable = {}
+
+        if isinstance(input, Input):
+            input_variable = input._to_dict()
+        elif isinstance(input, SweepDistribution):
+            if isinstance(input, Choice):
+                if input.values is not None:
+                    input_variable["type"] = cls._PYTHON_SDK_TYPE_MAPPING[type(input.values[0])]
+            elif isinstance(input, Randint):
+                input_variable["type"] = cls._PYTHON_SDK_TYPE_MAPPING[int]
+            else:
+                input_variable["type"] = cls._PYTHON_SDK_TYPE_MAPPING[float]
+
+            input_variable["optional"] = False
+        else:
+            input_variable["type"] = cls._PYTHON_SDK_TYPE_MAPPING[type(input)]
+            input_variable["default"] = input
+        return Input(**input_variable)
+
+    @classmethod
+    def _to_output(
+        cls,  # pylint: disable=unused-argument
+        output: Optional[Union[Output, Dict, str, bool, int, float]],
+        pipeline_job_dict: Optional[dict] = None,
+        **kwargs: Any,
+    ) -> Output:
+        """Translate output value to Output and infer component output type
+        from linked pipeline output, its original type or default type.
+
+        :param output: The output
+        :type output: Union[Output, str, bool, int, float]
+        :param pipeline_job_dict: The pipeline job dict
+        :type pipeline_job_dict: Optional[dict]
+        :return: The output object
+        :rtype: Output
+        """
+        pipeline_job_dict = pipeline_job_dict or {}
+        output_type = None
+        if not pipeline_job_dict or output is None:
+            try:
+                output_type = output.type  # type: ignore
+            except AttributeError:
+                # default to url_folder if failed to get type
+                output_type = AssetTypes.URI_FOLDER
+            output_variable = {"type": output_type}
+            return Output(**output_variable)
+        output_variable = {}
+
+        if isinstance(output, str) and bool(re.search(ComponentJobConstants.OUTPUT_PATTERN, output)):
+            # handle output bindings
+            output_variable["type"], output_variable["mode"] = cls._find_source_input_output_type(
+                output, pipeline_job_dict
+            )
+
+        elif isinstance(output, Output):
+            output_variable = output._to_dict()
+
+        elif isinstance(output, PipelineOutput):
+            output_variable = output._to_output()._to_dict()
+
+        elif type(output) in cls._PYTHON_SDK_TYPE_MAPPING:
+            output_variable["type"] = cls._PYTHON_SDK_TYPE_MAPPING[type(output)]
+            output_variable["default"] = output
+        else:
+            msg = "'{}' is not supported as component output, supported types are '{}'.".format(
+                type(output), cls._PYTHON_SDK_TYPE_MAPPING.keys()
+            )
+            raise JobException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        return Output(**output_variable)
+
+    def _to_inputs(self, inputs: Optional[Dict], **kwargs: Any) -> Dict:
+        """Translate inputs to Inputs.
+
+        :param inputs: mapping from input name to input object.
+        :type inputs: Dict[str, Union[Input, str, bool, int, float]]
+        :return: mapping from input name to translated component input.
+        :rtype: Dict[str, Input]
+        """
+        pipeline_job_dict = kwargs.get("pipeline_job_dict", {})
+        translated_component_inputs = {}
+        if inputs is not None:
+            for io_name, io_value in inputs.items():
+                translated_component_inputs[io_name] = self._to_input(io_value, pipeline_job_dict)
+        return translated_component_inputs
+
+    def _to_outputs(self, outputs: Optional[Dict], **kwargs: Any) -> Dict:
+        """Translate outputs to Outputs.
+
+        :param outputs: mapping from output name to output object.
+        :type outputs: Dict[str, Output]
+        :return: mapping from output name to translated component output.
+        :rtype: Dict[str, Output]
+        """
+        # Translate outputs to Outputs.
+        pipeline_job_dict = kwargs.get("pipeline_job_dict", {})
+        translated_component_outputs = {}
+        if outputs is not None:
+            for output_name, output_value in outputs.items():
+                translated_component_outputs[output_name] = self._to_output(output_value, pipeline_job_dict)
+        return translated_component_outputs
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> Union["Component", str]:
+        """Translate to Component.
+
+        :param context: The context
+        :type context: Optional[context]
+        :return: Translated Component.
+        :rtype: Component
+        """
+        # Note: Source of translated component should be same with Job
+        # And should be set after called _to_component/_to_node as job has no _source now.
+        raise NotImplementedError()
+
+    def _to_node(self, context: Optional[Dict] = None, **kwargs: Any) -> "BaseNode":
+        """Translate to pipeline node.
+
+        :param context: The context
+        :type context: Optional[context]
+        :return: Translated node.
+        :rtype: BaseNode
+        """
+        # Note: Source of translated component should be same with Job
+        # And should be set after called _to_component/_to_node as job has no _source now.
+        raise NotImplementedError()
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/__init__.py
new file mode 100644
index 00000000..3ccde947
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/__init__.py
@@ -0,0 +1,21 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+"""Classes in this package converts input & output set by user to pipeline job input & output."""
+
+from .attr_dict import OutputsAttrDict, _GroupAttrDict
+from .base import InputOutputBase, NodeInput, NodeOutput, PipelineInput, PipelineOutput
+from .mixin import AutoMLNodeIOMixin, NodeWithGroupInputMixin, PipelineJobIOMixin
+
+__all__ = [
+    "PipelineOutput",
+    "PipelineInput",
+    "NodeOutput",
+    "NodeInput",
+    "InputOutputBase",
+    "OutputsAttrDict",
+    "_GroupAttrDict",
+    "NodeWithGroupInputMixin",
+    "AutoMLNodeIOMixin",
+    "PipelineJobIOMixin",
+]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/attr_dict.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/attr_dict.py
new file mode 100644
index 00000000..0ae08bcd
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/attr_dict.py
@@ -0,0 +1,170 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from enum import Enum
+from typing import Any, Dict, List, Optional, Union
+
+from azure.ai.ml.entities._assets import Data
+from azure.ai.ml.entities._inputs_outputs import GroupInput, Input, Output
+from azure.ai.ml.entities._job.pipeline._attr_dict import K
+from azure.ai.ml.entities._job.pipeline._io.base import NodeInput, NodeOutput, PipelineInput
+from azure.ai.ml.exceptions import (
+    ErrorCategory,
+    ErrorTarget,
+    UnexpectedAttributeError,
+    UnexpectedKeywordError,
+    ValidationException,
+)
+
+
+class InputsAttrDict(dict):
+    def __init__(self, inputs: dict, **kwargs: Any):
+        self._validate_inputs(inputs)
+        super(InputsAttrDict, self).__init__(**inputs, **kwargs)
+
+    @classmethod
+    def _validate_inputs(cls, inputs: Any) -> None:
+        msg = "Pipeline/component input should be a \
+        azure.ai.ml.entities._job.pipeline._io.NodeInput with owner, got {}."
+        for val in inputs.values():
+            if isinstance(val, NodeInput) and val._owner is not None:  # pylint: disable=protected-access
+                continue
+            if isinstance(val, _GroupAttrDict):
+                continue
+            raise ValidationException(
+                message=msg.format(val),
+                no_personal_data_message=msg.format("[val]"),
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    def __setattr__(
+        self,
+        key: str,
+        value: Union[int, bool, float, str, NodeOutput, PipelineInput, Input],
+    ) -> None:
+        # Extract enum value.
+        value = value.value if isinstance(value, Enum) else value
+        original_input = self.__getattr__(key)  # Note that an exception will be raised if the keyword is invalid.
+        if isinstance(original_input, _GroupAttrDict) or isinstance(value, _GroupAttrDict):
+            # Set the value directly if is parameter group.
+            self._set_group_with_type_check(key, GroupInput.custom_class_value_to_attr_dict(value))
+            return
+        original_input._data = original_input._build_data(value)
+
+    def _set_group_with_type_check(self, key: Any, value: Any) -> None:
+        msg = "{!r} is expected to be a parameter group, but got {}."
+        if not isinstance(value, _GroupAttrDict):
+            raise ValidationException(
+                message=msg.format(key, type(value)),
+                no_personal_data_message=msg.format("[key]", "[value_type]"),
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        self.__setitem__(key, GroupInput.custom_class_value_to_attr_dict(value))
+
+    def __getattr__(self, item: Any) -> NodeInput:
+        res: NodeInput = self.__getitem__(item)
+        return res
+
+
+class _GroupAttrDict(InputsAttrDict):
+    """This class is used for accessing values with instance.some_key."""
+
+    @classmethod
+    def _validate_inputs(cls, inputs: Any) -> None:
+        msg = "Pipeline/component input should be a azure.ai.ml.entities._job.pipeline._io.NodeInput, got {}."
+        for val in inputs.values():
+            if isinstance(val, NodeInput) and val._owner is not None:  # pylint: disable=protected-access
+                continue
+            if isinstance(val, _GroupAttrDict):
+                continue
+            # Allow PipelineInput as Group may appear at top level pipeline input.
+            if isinstance(val, PipelineInput):
+                continue
+            raise ValidationException(
+                message=msg.format(val),
+                no_personal_data_message=msg.format("[val]"),
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    def __getattr__(self, name: K) -> Any:
+        if name not in self:
+            raise UnexpectedAttributeError(keyword=name, keywords=list(self))
+        return super().__getitem__(name)
+
+    def __getitem__(self, item: K) -> Any:
+        # We raise this exception instead of KeyError
+        if item not in self:
+            raise UnexpectedKeywordError(func_name="ParameterGroup", keyword=item, keywords=list(self))
+        return super().__getitem__(item)
+
+    # For Jupyter Notebook auto-completion
+    def __dir__(self) -> List:
+        return list(super().__dir__()) + list(self.keys())
+
+    def flatten(self, group_parameter_name: Optional[str]) -> Dict:
+        # Return the flattened result of self
+
+        group_parameter_name = group_parameter_name if group_parameter_name else ""
+        flattened_parameters = {}
+        msg = "'%s' in parameter group should be a azure.ai.ml.entities._job._io.NodeInput, got '%s'."
+        for k, v in self.items():
+            flattened_name = ".".join([group_parameter_name, k])
+            if isinstance(v, _GroupAttrDict):
+                flattened_parameters.update(v.flatten(flattened_name))
+            elif isinstance(v, NodeInput):
+                flattened_parameters[flattened_name] = v._to_job_input()  # pylint: disable=protected-access
+            else:
+                raise ValidationException(
+                    message=msg % (flattened_name, type(v)),
+                    no_personal_data_message=msg % ("name", "type"),
+                    target=ErrorTarget.PIPELINE,
+                )
+        return flattened_parameters
+
+    def insert_group_name_for_items(self, group_name: Any) -> None:
+        # Insert one group name for all items.
+        for v in self.values():
+            if isinstance(v, _GroupAttrDict):
+                v.insert_group_name_for_items(group_name)
+            elif isinstance(v, PipelineInput):
+                # Insert group names for pipeline input
+                v._group_names = [group_name] + v._group_names  # pylint: disable=protected-access
+
+
+class OutputsAttrDict(dict):
+    def __init__(self, outputs: dict, **kwargs: Any):
+        for val in outputs.values():
+            if not isinstance(val, NodeOutput) or val._owner is None:
+                msg = "Pipeline/component output should be a azure.ai.ml.dsl.Output with owner, got {}."
+                raise ValidationException(
+                    message=msg.format(val),
+                    no_personal_data_message=msg.format("[val]"),
+                    target=ErrorTarget.PIPELINE,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+        super(OutputsAttrDict, self).__init__(**outputs, **kwargs)
+
+    def __getattr__(self, item: Any) -> NodeOutput:
+        return self.__getitem__(item)
+
+    def __getitem__(self, item: Any) -> NodeOutput:
+        if item not in self:
+            # We raise this exception instead of KeyError as OutputsAttrDict doesn't support add new item after
+            # __init__.
+            raise UnexpectedAttributeError(keyword=item, keywords=list(self))
+        res: NodeOutput = super().__getitem__(item)
+        return res
+
+    def __setattr__(self, key: str, value: Union[Data, Output]) -> None:
+        if isinstance(value, Output):
+            mode = value.mode
+            value = Output(type=value.type, path=value.path, mode=mode, name=value.name, version=value.version)
+        original_output = self.__getattr__(key)  # Note that an exception will be raised if the keyword is invalid.
+        original_output._data = original_output._build_data(value)
+
+    def __setitem__(self, key: str, value: Output) -> None:
+        return self.__setattr__(key, value)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/base.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/base.py
new file mode 100644
index 00000000..b17972ae
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/base.py
@@ -0,0 +1,848 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import copy
+import re
+from abc import ABC, abstractmethod
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Set, TypeVar, Union, cast, overload
+
+from azure.ai.ml._utils.utils import is_data_binding_expression
+from azure.ai.ml.constants import AssetTypes
+from azure.ai.ml.constants._component import IOConstants
+from azure.ai.ml.entities._assets._artifacts.data import Data
+from azure.ai.ml.entities._assets._artifacts.model import Model
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._job.pipeline._pipeline_expression import PipelineExpressionMixin
+from azure.ai.ml.entities._util import resolve_pipeline_parameter
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, UserErrorException, ValidationException
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities import PipelineJob
+    from azure.ai.ml.entities._builders import BaseNode
+
+T = TypeVar("T")
+
+
+def _build_data_binding(data: Union[str, "PipelineInput", "Output"]) -> Union[str, Output]:
+    """Build input builders to data bindings.
+
+    :param data: The data to build a data binding from
+    :type data: Union[str, PipelineInput, Output]
+    :return: A data binding string if data isn't a str, otherwise data
+    :rtype: str
+    """
+    result: Union[str, Output] = ""
+
+    if isinstance(data, (InputOutputBase)):
+        # Build data binding when data is PipelineInput, Output
+        result = data._data_binding()
+    else:
+        # Otherwise just return the data
+        result = data
+    return result
+
+
+def _resolve_builders_2_data_bindings(
+    data: Union[list, dict, str, "PipelineInput", "Output"]
+) -> Union[dict, list, str, Output]:
+    """Traverse data and build input builders inside it to data bindings.
+
+    :param data: The bindings to resolve
+    :type data: Union[list, dict, str, "PipelineInput", "Output"]
+    :return:
+       * A dict if data was a dict
+       * A list if data was a list
+       * A str otherwise
+    :rtype: Union[list, dict, str]
+    """
+    if isinstance(data, dict):
+        for key, val in data.items():
+            if isinstance(val, (dict, list)):
+                data[key] = _resolve_builders_2_data_bindings(val)
+            else:
+                data[key] = _build_data_binding(val)
+        return data
+    if isinstance(data, list):
+        resolved_data = []
+        for val in data:
+            resolved_data.append(_resolve_builders_2_data_bindings(val))
+        return resolved_data
+    return _build_data_binding(data)
+
+
+def _data_to_input(data: Union[Data, Model]) -> Input:
+    """Convert a Data object to an Input object.
+
+    :param data: The data to convert
+    :type data: Data
+    :return: The Input object
+    :rtype: Input
+    """
+    if data.id:
+        return Input(type=data.type, path=data.id)
+    return Input(type=data.type, path=f"{data.name}:{data.version}")
+
+
+class InputOutputBase(ABC):
+    # TODO: refine this code, always use _data to store builder level settings and use _meta to store definition
+    # TODO: when _data missing, return value from _meta
+
+    def __init__(
+        self,
+        meta: Optional[Union[Input, Output]],
+        data: Optional[Union[int, bool, float, str, Input, Output, "PipelineInput"]],
+        default_data: Optional[Union[int, bool, float, str, Input, Output]] = None,
+        **kwargs: Any,
+    ):
+        """Base class of input & output.
+
+        :param meta: Metadata of this input/output, eg: type, min, max, etc.
+        :type meta: Union[Input, Output]
+        :param data: Actual value of input/output, None means un-configured data.
+        :type data: Union[None, int, bool, float, str,
+                          azure.ai.ml.Input,
+                          azure.ai.ml.Output]
+        :param default_data: default value of input/output, None means un-configured data.
+        :type default_data: Union[None, int, bool, float, str,
+                          azure.ai.ml.Input,
+                          azure.ai.ml.Output]
+        """
+        self._meta = meta
+        self._original_data = data
+        self._data: Any = self._build_data(data)
+        self._default_data = default_data
+        self._type: str = meta.type if meta is not None else kwargs.pop("type", None)
+        self._mode = self._get_mode(original_data=data, data=self._data, kwargs=kwargs)
+        self._description = (
+            self._data.description
+            if self._data is not None and hasattr(self._data, "description") and self._data.description
+            else kwargs.pop("description", None)
+        )
+        # TODO: remove this
+        self._attribute_map: Dict = {}
+        self._name: Optional[str] = ""
+        self._version: Optional[str] = ""
+        super(InputOutputBase, self).__init__(**kwargs)
+
+    @abstractmethod
+    def _build_data(self, data: T) -> Union[T, str, Input, "InputOutputBase"]:
+        """Validate if data matches type and translate it to Input/Output acceptable type.
+
+        :param data: The data
+        :type data: T
+        :return: The built data
+        :rtype: Union[T, str, Input, InputOutputBase]
+        """
+
+    @abstractmethod
+    def _build_default_data(self) -> None:
+        """Build default data when data not configured."""
+
+    @property
+    def type(self) -> str:
+        """Type of input/output.
+
+        :return: The type
+        :rtype: str
+        """
+        return self._type
+
+    @type.setter
+    def type(self, type: Any) -> None:  # pylint: disable=redefined-builtin
+        # For un-configured input/output, we build a default data entry for them.
+        self._build_default_data()
+        self._type = type
+        if isinstance(self._data, (Input, Output)):
+            self._data.type = type
+        elif self._data is not None and not isinstance(
+            self._data, (int, float, str)
+        ):  # when type of self._data is InputOutputBase or its child class
+            self._data._type = type
+
+    @property
+    def mode(self) -> Optional[str]:
+        return self._mode
+
+    @mode.setter
+    def mode(self, mode: Optional[str]) -> None:
+        # For un-configured input/output, we build a default data entry for them.
+        self._build_default_data()
+        self._mode = mode
+        if isinstance(self._data, (Input, Output)):
+            self._data.mode = mode
+        elif self._data is not None and not isinstance(self._data, (int, float, str)):
+            self._data._mode = mode
+
+    @property
+    def description(self) -> Any:
+        return self._description
+
+    @description.setter
+    def description(self, description: str) -> None:
+        # For un-configured input/output, we build a default data entry for them.
+        self._build_default_data()
+        self._description = description
+        if isinstance(self._data, (Input, Output)):
+            self._data.description = description
+        elif self._data is not None and not isinstance(self._data, (int, float, str)):
+            self._data._description = description
+
+    @property
+    def path(self) -> Optional[str]:
+        # This property is introduced for static intellisense.
+        if hasattr(self._data, "path"):
+            if self._data is not None and not isinstance(self._data, (int, float, str)):
+                res: Optional[str] = self._data.path
+                return res
+        msg = f"{type(self._data)} does not have path."
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.PIPELINE,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+    @path.setter
+    def path(self, path: str) -> None:
+        # For un-configured input/output, we build a default data entry for them.
+        self._build_default_data()
+        if hasattr(self._data, "path"):
+            if self._data is not None and not isinstance(self._data, (int, float, str)):
+                self._data.path = path
+        else:
+            msg = f"{type(self._data)} does not support setting path."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    def _data_binding(self) -> str:
+        """Return data binding string representation for this input/output.
+
+        :return: The data binding string
+        :rtype: str
+        """
+        raise NotImplementedError()
+
+    # Why did we have this function? It prevents the DictMixin from being applied.
+    # Unclear if we explicitly do NOT want the mapping protocol to be applied to this, or it this was just
+    # confirmation that it didn't at the time.
+    def keys(self) -> None:
+        # This property is introduced to raise catchable Exception in marshmallow mapping validation trial.
+        raise TypeError(f"'{type(self).__name__}' object is not a mapping")
+
+    def __str__(self) -> str:
+        try:
+            return self._data_binding()
+        except AttributeError:
+            return super(InputOutputBase, self).__str__()
+
+    def __hash__(self) -> int:
+        return id(self)
+
+    @classmethod
+    def _get_mode(
+        cls,
+        original_data: Optional[Union[int, bool, float, str, Input, Output, "PipelineInput"]],
+        data: Optional[Union[int, bool, float, str, Input, Output]],
+        kwargs: dict,
+    ) -> Optional[str]:
+        """Get mode of this input/output builder.
+
+        :param original_data: Original value of input/output.
+        :type original_data: Union[None, int, bool, float, str
+                          azure.ai.ml.Input,
+                          azure.ai.ml.Output,
+                          azure.ai.ml.entities._job.pipeline._io.PipelineInput]
+        :param data: Built input/output data.
+        :type data: Union[None, int, bool, float, str
+                          azure.ai.ml.Input,
+                          azure.ai.ml.Output]
+        :param kwargs: The kwargs
+        :type kwargs: Dict
+        :return: The mode
+        :rtype: Optional[str]
+        """
+        # pipeline level inputs won't pass mode to bound node level inputs
+        if isinstance(original_data, PipelineInput):
+            return None
+        return data.mode if data is not None and hasattr(data, "mode") else kwargs.pop("mode", None)
+
+    @property
+    def _is_primitive_type(self) -> bool:
+        return self.type in IOConstants.PRIMITIVE_STR_2_TYPE
+
+
+class NodeInput(InputOutputBase):
+    """Define one input of a Component."""
+
+    def __init__(
+        self,
+        port_name: str,
+        meta: Optional[Input],
+        *,
+        data: Optional[Union[int, bool, float, str, Output, "PipelineInput", Input]] = None,
+        # TODO: Bug Item number: 2883405
+        owner: Optional[Union["BaseComponent", "PipelineJob"]] = None,  # type: ignore
+        **kwargs: Any,
+    ):
+        """Initialize an input of a component.
+
+        :param name: The name of the input.
+        :type name: str
+        :param meta: Metadata of this input, eg: type, min, max, etc.
+        :type meta: Input
+        :param data: The input data. Valid types include int, bool, float, str,
+            Output of another component or pipeline input and Input.
+            Note that the output of another component or pipeline input associated should be reachable in the scope
+            of current pipeline. Input is introduced to support case like
+            TODO: new examples
+            component.inputs.xxx = Input(path="arm_id")
+        :type data: Union[int, bool, float, str
+                          azure.ai.ml.Output,
+                          azure.ai.ml.Input]
+        :param owner: The owner component of the input, used to calculate binding.
+        :type owner: Union[azure.ai.ml.entities.BaseNode, azure.ai.ml.entities.PipelineJob]
+        :param kwargs: A dictionary of additional configuration parameters.
+        :type kwargs: dict
+        """
+        # TODO: validate data matches type in meta
+        # TODO: validate supported data
+        self._port_name = port_name
+        self._owner = owner
+        super().__init__(meta=meta, data=data, **kwargs)
+
+    def _build_default_data(self) -> None:
+        """Build default data when input not configured."""
+        if self._data is None:
+            self._data = Input()
+
+    def _build_data(self, data: T) -> Union[T, str, Input, InputOutputBase]:
+        """Build input data according to assigned input
+
+        eg: node.inputs.key = data
+
+        :param data: The data
+        :type data: T
+        :return: The built data
+        :rtype: Union[T, str, Input, "PipelineInput", "NodeOutput"]
+        """
+        _data: Union[T, str, NodeOutput] = resolve_pipeline_parameter(data)
+        if _data is None:
+            return _data
+        # Unidiomatic typecheck: Checks that data is _exactly_ this type, and not potentially a subtype
+        if type(_data) is NodeInput:  # pylint: disable=unidiomatic-typecheck
+            msg = "Can not bind input to another component's input."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        if isinstance(_data, (PipelineInput, NodeOutput)):
+            # If value is input or output, it's a data binding, we require it have a owner so we can convert it to
+            # a data binding, eg: ${{inputs.xxx}}
+            if isinstance(_data, NodeOutput) and _data._owner is None:
+                msg = "Setting input binding {} to output without owner is not allowed."
+                raise ValidationException(
+                    message=msg.format(_data),
+                    no_personal_data_message=msg.format("[_data]"),
+                    target=ErrorTarget.PIPELINE,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            return _data
+        # for data binding case, set is_singular=False for case like "${{parent.inputs.job_in_folder}}/sample1.csv"
+        if isinstance(_data, Input) or is_data_binding_expression(_data, is_singular=False):
+            return _data
+        if isinstance(_data, (Data, Model)):
+            return _data_to_input(_data)
+        # self._meta.type could be None when sub pipeline has no annotation
+        if isinstance(self._meta, Input) and self._meta.type and not self._meta._is_primitive_type:
+            if isinstance(_data, str):
+                return Input(type=self._meta.type, path=_data)
+            msg = "only path input is supported now but get {}: {}."
+            raise UserErrorException(
+                message=msg.format(type(_data), _data),
+                no_personal_data_message=msg.format(type(_data), "[_data]"),
+            )
+        return _data
+
+    def _to_job_input(self) -> Optional[Union[Input, str, Output]]:
+        """convert the input to Input, this logic will change if backend contract changes."""
+        result: Optional[Union[Input, str, Output]] = None
+
+        if self._data is None:
+            # None data means this input is not configured.
+            result = None
+        elif isinstance(self._data, (PipelineInput, NodeOutput)):
+            # Build data binding when data is PipelineInput, Output
+            result = Input(path=self._data._data_binding(), mode=self.mode)
+        elif is_data_binding_expression(self._data):
+            result = Input(path=self._data, mode=self.mode)
+        else:
+            data_binding = _build_data_binding(self._data)
+            if is_data_binding_expression(self._data):
+                result = Input(path=data_binding, mode=self.mode)
+            else:
+                result = data_binding
+            # TODO: validate is self._data is supported
+
+        return result
+
+    def _data_binding(self) -> str:
+        msg = "Input binding {} can only come from a pipeline, currently got {}"
+        # call type(self._owner) to avoid circular import
+        raise ValidationException(
+            message=msg.format(self._port_name, type(self._owner)),
+            target=ErrorTarget.PIPELINE,
+            no_personal_data_message=msg.format("[port_name]", "[owner]"),
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+    def _copy(self, owner: Any) -> "NodeInput":
+        return NodeInput(
+            port_name=self._port_name,
+            data=self._data,
+            owner=owner,
+            meta=cast(Input, self._meta),
+        )
+
+    def _deepcopy(self) -> "NodeInput":
+        return NodeInput(
+            port_name=self._port_name,
+            data=copy.copy(self._data),
+            owner=self._owner,
+            meta=cast(Input, self._meta),
+        )
+
+    def _get_data_owner(self) -> Optional["BaseNode"]:
+        """Gets the data owner of the node
+
+        Note: This only works for @pipeline, not for YAML pipeline.
+
+        Note: Inner step will be returned as the owner when node's input is from sub pipeline's output.
+            @pipeline
+            def sub_pipeline():
+                inner_node = component_func()
+                return inner_node.outputs
+
+            @pipeline
+            def root_pipeline():
+                pipeline_node = sub_pipeline()
+                node = copy_files_component_func(input_dir=pipeline_node.outputs.output_dir)
+                owner = node.inputs.input_dir._get_data_owner()
+                assert owner == pipeline_node.nodes[0]
+
+        :return: The node if Input is from another node's output. Returns None for literal value.
+        :rtype: Optional[BaseNode]
+        """
+        from azure.ai.ml.entities import Pipeline
+        from azure.ai.ml.entities._builders import BaseNode
+
+        def _resolve_data_owner(data: Any) -> Optional["BaseNode"]:
+            if isinstance(data, BaseNode) and not isinstance(data, Pipeline):
+                return data
+            while isinstance(data, PipelineInput):
+                # for pipeline input, it's original value(can be literal value or another node's output)
+                # is stored in _original_data
+                return _resolve_data_owner(data._original_data)
+            if isinstance(data, NodeOutput):
+                if isinstance(data._owner, Pipeline):
+                    # for input from subgraph's output, trace back to inner node
+                    return _resolve_data_owner(data._binding_output)
+                # for input from another node's output, return the node
+                return _resolve_data_owner(data._owner)
+            return None
+
+        return _resolve_data_owner(self._data)
+
+
+class NodeOutput(InputOutputBase, PipelineExpressionMixin):
+    """Define one output of a Component."""
+
+    def __init__(
+        self,
+        port_name: str,
+        meta: Optional[Union[Input, Output]],
+        *,
+        data: Optional[Union[Output, str]] = None,
+        # TODO: Bug Item number: 2883405
+        owner: Optional[Union["BaseComponent", "PipelineJob"]] = None,  # type: ignore
+        binding_output: Optional["NodeOutput"] = None,
+        **kwargs: Any,
+    ):
+        """Initialize an Output of a component.
+
+        :param port_name: The port_name of the output.
+        :type port_name: str
+        :param name: The name used to register NodeOutput/PipelineOutput data.
+        :type name: str
+        :param version: The version used to register NodeOutput/PipelineOutput data.
+        :ype version: str
+        :param data: The output data. Valid types include str, Output
+        :type data: Union[str
+                          azure.ai.ml.entities.Output]
+        :param mode: The mode of the output.
+        :type mode: str
+        :param owner: The owner component of the output, used to calculate binding.
+        :type owner: Union[azure.ai.ml.entities.BaseNode, azure.ai.ml.entities.PipelineJob]
+        :param binding_output: The node output bound to pipeline output, only available for pipeline.
+        :type binding_output: azure.ai.ml.entities.NodeOutput
+        :param kwargs: A dictionary of additional configuration parameters.
+        :type kwargs: dict
+        :raises ~azure.ai.ml.exceptions.ValidationException: Raised if object cannot be successfully validated.
+            Details will be provided in the error message.
+        """
+        # Allow inline output binding with string, eg: "component_out_path_1": "${{parents.outputs.job_out_data_1}}"
+        if data is not None and not isinstance(data, (Output, str)):
+            msg = "Got unexpected type for output: {}."
+            raise ValidationException(
+                message=msg.format(data),
+                target=ErrorTarget.PIPELINE,
+                no_personal_data_message=msg.format("[data]"),
+            )
+        super().__init__(meta=meta, data=data, **kwargs)
+        self._port_name = port_name
+        self._owner = owner
+        self._name: Optional[str] = self._data.name if isinstance(self._data, Output) else None
+        self._version: Optional[str] = self._data.version if isinstance(self._data, Output) else None
+
+        self._assert_name_and_version()
+
+        # store original node output to be able to trace back to inner node from a pipeline output builder.
+        self._binding_output = binding_output
+
+    @property
+    def port_name(self) -> str:
+        """The output port name, eg: node.outputs.port_name.
+
+        :return: The port name
+        :rtype: str
+        """
+        return self._port_name
+
+    @property
+    def name(self) -> Optional[str]:
+        """Used in registering output data.
+
+        :return: The output name
+        :rtype: str
+        """
+        return self._name
+
+    @name.setter
+    def name(self, name: str) -> None:
+        """Assigns the name to NodeOutput/PipelineOutput and builds data according to the name.
+
+        :param name: The new name
+        :type name: str
+        """
+        self._build_default_data()
+        self._name = name
+        if isinstance(self._data, Output):
+            self._data.name = name
+        elif isinstance(self._data, InputOutputBase):
+            self._data._name = name
+        else:
+            raise UserErrorException(
+                f"We support self._data of Input, Output, InputOutputBase, NodeOutput and NodeInput,"
+                f"but got type: {type(self._data)}."
+            )
+
+    @property
+    def version(self) -> Optional[str]:
+        """Used in registering output data.
+
+        :return: The output data
+        :rtype: str
+        """
+        return self._version
+
+    @version.setter
+    def version(self, version: str) -> None:
+        """Assigns the version to NodeOutput/PipelineOutput and builds data according to the version.
+
+        :param version: The new version
+        :type version: str
+        """
+        self._build_default_data()
+        self._version = version
+        if isinstance(self._data, Output):
+            self._data.version = version
+        elif isinstance(self._data, InputOutputBase):
+            self._data._version = version
+        else:
+            raise UserErrorException(
+                f"We support self._data of Input, Output, InputOutputBase, NodeOutput and NodeInput,"
+                f"but got type: {type(self._data)}."
+            )
+
+    @property
+    def path(self) -> Any:
+        # For node output path,
+        if self._data is not None and hasattr(self._data, "path"):
+            return self._data.path
+        return None
+
+    @path.setter
+    def path(self, path: Optional[str]) -> None:
+        # For un-configured output, we build a default data entry for them.
+        self._build_default_data()
+        if self._data is not None and hasattr(self._data, "path"):
+            self._data.path = path
+        else:
+            # YAML job will have string output binding and do not support setting path for it.
+            msg = f"{type(self._data)} does not support setting path."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    def _assert_name_and_version(self) -> None:
+        if self.name and not (re.match("^[A-Za-z0-9_-]*$", self.name) and len(self.name) <= 255):
+            raise UserErrorException(
+                f"The output name {self.name} can only contain alphanumeric characters, dashes and underscores, "
+                f"with a limit of 255 characters."
+            )
+        if self.version and not self.name:
+            raise UserErrorException("Output name is required when output version is specified.")
+
+    def _build_default_data(self) -> None:
+        """Build default data when output not configured."""
+        if self._data is None:
+            # _meta will be None when node._component is not a Component object
+            # so we just leave the type inference work to backend
+            self._data = Output(type=None)  # type: ignore[call-overload]
+
+    def _build_data(self, data: T) -> Any:
+        """Build output data according to assigned input, eg: node.outputs.key = data
+
+        :param data: The data
+        :type data: T
+        :return: `data`
+        :rtype: T
+        """
+        if data is None:
+            return data
+        if not isinstance(data, (Output, str)):
+            msg = f"{self.__class__.__name__} only allow set {Output.__name__} object, {type(data)} is not supported."
+            raise ValidationException(
+                message=msg,
+                target=ErrorTarget.PIPELINE,
+                no_personal_data_message=msg,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        res: T = cast(T, data)
+        return res
+
+    def _to_job_output(self) -> Optional[Output]:
+        """Convert the output to Output, this logic will change if backend contract changes."""
+        if self._data is None:
+            # None data means this output is not configured.
+            result = None
+        elif isinstance(self._data, str):
+            result = Output(
+                type=AssetTypes.URI_FOLDER, path=self._data, mode=self.mode, name=self.name, version=self.version
+            )
+        elif isinstance(self._data, Output):
+            result = self._data
+        elif isinstance(self._data, PipelineOutput):
+            result = Output(
+                type=AssetTypes.URI_FOLDER,
+                path=self._data._data_binding(),
+                mode=self.mode,
+                name=self._data.name,
+                version=self._data.version,
+                description=self.description,
+            )
+        else:
+            msg = "Got unexpected type for output: {}."
+            raise ValidationException(
+                message=msg.format(self._data),
+                target=ErrorTarget.PIPELINE,
+                no_personal_data_message=msg.format("[data]"),
+            )
+        return result
+
+    def _data_binding(self) -> str:
+        if self._owner is not None:
+            return f"${{{{parent.jobs.{self._owner.name}.outputs.{self._port_name}}}}}"
+
+        return ""
+
+    def _copy(self, owner: Any) -> "NodeOutput":
+        return NodeOutput(
+            port_name=self._port_name,
+            data=cast(Output, self._data),
+            owner=owner,
+            meta=self._meta,
+        )
+
+    def _deepcopy(self) -> "NodeOutput":
+        return NodeOutput(
+            port_name=self._port_name,
+            data=cast(Output, copy.copy(self._data)),
+            owner=self._owner,
+            meta=self._meta,
+            binding_output=self._binding_output,
+        )
+
+
+class PipelineInput(NodeInput, PipelineExpressionMixin):
+    """Define one input of a Pipeline."""
+
+    def __init__(self, name: str, meta: Optional[Input], group_names: Optional[List[str]] = None, **kwargs: Any):
+        """Initialize a PipelineInput.
+
+        :param name: The name of the input.
+        :type name: str
+        :param meta: Metadata of this input, eg: type, min, max, etc.
+        :type meta: Input
+        :param group_names: The input parameter's group names.
+        :type group_names: List[str]
+        """
+        super(PipelineInput, self).__init__(port_name=name, meta=meta, **kwargs)
+        self._group_names = group_names if group_names else []
+
+    def result(self) -> Any:
+        """Return original value of pipeline input.
+
+        :return: The original value of pipeline input
+        :rtype: Any
+
+        Example:
+
+        .. code-block:: python
+
+           @pipeline
+           def pipeline_func(param1):
+             # node1's param1 will get actual value of param1 instead of a input binding.
+             node1 = component_func(param1=param1.result())
+        """
+
+        # use this to break self loop
+        original_data_cache: Set = set()
+        original_data = self._original_data
+        while isinstance(original_data, PipelineInput) and original_data not in original_data_cache:
+            original_data_cache.add(original_data)
+            original_data = original_data._original_data
+        return original_data
+
+    def __str__(self) -> str:
+        return self._data_binding()
+
+    @overload
+    def _build_data(self, data: Union[Model, Data]) -> Input: ...
+
+    @overload
+    def _build_data(self, data: T) -> Any: ...
+
+    def _build_data(self, data: Union[Model, Data, T]) -> Any:
+        """Build data according to input type.
+
+        :param data: The data
+        :type data: Union[Model, Data, T]
+        :return:
+            * Input if data is a Model or Data
+            * data otherwise
+        :rtype: Union[Input, T]
+        """
+        if data is None:
+            return data
+        # Unidiomatic typecheck: Checks that data is _exactly_ this type, and not potentially a subtype
+        if type(data) is NodeInput:  # pylint: disable=unidiomatic-typecheck
+            msg = "Can not bind input to another component's input."
+            raise ValidationException(message=msg, no_personal_data_message=msg, target=ErrorTarget.PIPELINE)
+        if isinstance(data, (PipelineInput, NodeOutput)):
+            # If value is input or output, it's a data binding, owner is required to convert it to
+            # a data binding, eg: ${{parent.inputs.xxx}}
+            if isinstance(data, NodeOutput) and data._owner is None:
+                msg = "Setting input binding {} to output without owner is not allowed."
+                raise ValidationException(
+                    message=msg.format(data),
+                    no_personal_data_message=msg.format("[data]"),
+                    target=ErrorTarget.PIPELINE,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            return data
+        if isinstance(data, (Data, Model)):
+            # If value is Data, we convert it to an corresponding Input
+            return _data_to_input(data)
+        return data
+
+    def _data_binding(self) -> str:
+        full_name = "%s.%s" % (".".join(self._group_names), self._port_name) if self._group_names else self._port_name
+        return f"${{{{parent.inputs.{full_name}}}}}"
+
+    def _to_input(self) -> Optional[Union[Input, Output]]:
+        """Convert pipeline input to component input for pipeline component.
+
+        :return: The component input
+        :rtype: Input
+        """
+        if self._data is None:
+            # None data means this input is not configured.
+            return self._meta
+        data_type = self._data.type if isinstance(self._data, Input) else None
+        # If type is asset type, return data type without default.
+        # Else infer type from data and set it as default.
+        if data_type and data_type.lower() in AssetTypes.__dict__.values():
+            if not isinstance(self._data, (int, float, str)):
+                result = Input(type=data_type, mode=self._data.mode)
+        elif type(self._data) in IOConstants.PRIMITIVE_TYPE_2_STR:
+            result = Input(
+                type=IOConstants.PRIMITIVE_TYPE_2_STR[type(self._data)],
+                default=self._data,
+            )
+        else:
+            msg = f"Unsupported Input type {type(self._data)} detected when translate job to component."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        return result  # pylint: disable=possibly-used-before-assignment
+
+
+class PipelineOutput(NodeOutput):
+    """Define one output of a Pipeline."""
+
+    def _to_job_output(self) -> Optional[Output]:
+        result: Optional[Output] = None
+        if isinstance(self._data, Output):
+            # For pipeline output with type Output, always pass to backend.
+            result = self._data
+        elif self._data is None and self._meta and self._meta.type:
+            # For un-configured pipeline output with meta, we need to return Output with accurate type,
+            # so it won't default to uri_folder.
+            result = Output(type=self._meta.type, mode=self._meta.mode, description=self._meta.description)
+        else:
+            result = super(PipelineOutput, self)._to_job_output()
+        # Copy meta type to avoid built output's None type default to uri_folder.
+        if self.type and result is not None and not result.type:
+            result.type = self.type
+        return result
+
+    def _data_binding(self) -> str:
+        return f"${{{{parent.outputs.{self._port_name}}}}}"
+
+    def _to_output(self) -> Optional[Output]:
+        """Convert pipeline output to component output for pipeline component."""
+        if self._data is None:
+            # None data means this input is not configured.
+            return None
+        if isinstance(self._meta, Output):
+            return self._meta
+        # Assign type directly as we didn't have primitive output type for now.
+        if not isinstance(self._data, (int, float, str)):
+            return Output(type=self._data.type, mode=self._data.mode)
+        return Output()
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/mixin.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/mixin.py
new file mode 100644
index 00000000..6c3d9357
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_io/mixin.py
@@ -0,0 +1,623 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import copy
+from typing import Any, Dict, List, Optional, Tuple, Type, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobInput as RestJobInput
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobOutput as RestJobOutput
+from azure.ai.ml.constants._component import ComponentJobConstants
+from azure.ai.ml.entities._inputs_outputs import GroupInput, Input, Output
+from azure.ai.ml.entities._util import copy_output_setting
+from azure.ai.ml.exceptions import ErrorTarget, ValidationErrorType, ValidationException
+
+from ..._input_output_helpers import (
+    from_rest_data_outputs,
+    from_rest_inputs_to_dataset_literal,
+    to_rest_data_outputs,
+    to_rest_dataset_literal_inputs,
+)
+from .._pipeline_job_helpers import from_dict_to_rest_io, process_sdk_component_job_io
+from .attr_dict import InputsAttrDict, OutputsAttrDict, _GroupAttrDict
+from .base import NodeInput, NodeOutput, PipelineInput, PipelineOutput
+
+
+class NodeIOMixin:
+    """Provides ability to wrap node inputs/outputs and build data bindings
+    dynamically."""
+
+    @classmethod
+    def _get_supported_inputs_types(cls) -> Optional[Any]:
+        return None
+
+    @classmethod
+    def _get_supported_outputs_types(cls) -> Optional[Any]:
+        return None
+
+    @classmethod
+    def _validate_io(cls, value: Any, allowed_types: Optional[tuple], *, key: Optional[str] = None) -> None:
+        if allowed_types is None:
+            return
+
+        if value is None or isinstance(value, allowed_types):
+            pass
+        else:
+            msg = "Expecting {} for input/output {}, got {} instead."
+            raise ValidationException(
+                message=msg.format(allowed_types, key, type(value)),
+                no_personal_data_message=msg.format(allowed_types, "[key]", type(value)),
+                target=ErrorTarget.PIPELINE,
+                error_type=ValidationErrorType.INVALID_VALUE,
+            )
+
+    def _build_input(
+        self,
+        name: str,
+        meta: Optional[Input],
+        data: Optional[Union[dict, int, bool, float, str, Output, "PipelineInput", Input]],
+    ) -> NodeInput:
+        # output mode of last node should not affect input mode of next node
+        if isinstance(data, NodeOutput):
+            # Decoupled input and output
+            # value = copy.deepcopy(value)
+            data = data._deepcopy()  # pylint: disable=protected-access
+            data.mode = None
+        elif isinstance(data, dict):
+            # Use type comparison instead of is_instance to skip _GroupAttrDict
+            # when loading from yaml io will be a dict,
+            # like {'job_data_path': '${{parent.inputs.pipeline_job_data_path}}'}
+            # parse dict to allowed type
+            data = Input(**data)
+
+            # parameter group can be of custom type, so we don't check it here
+            if meta is not None and not isinstance(meta, GroupInput):
+                self._validate_io(data, self._get_supported_inputs_types(), key=name)
+        return NodeInput(port_name=name, meta=meta, data=data, owner=self)
+
+    def _build_output(self, name: str, meta: Optional[Output], data: Optional[Union[Output, str]]) -> NodeOutput:
+        if isinstance(data, dict):
+            data = Output(**data)
+
+        self._validate_io(data, self._get_supported_outputs_types(), key=name)
+        # For un-configured outputs, settings it to None, so we won't pass extra fields(eg: default mode)
+        return NodeOutput(port_name=name, meta=meta, data=data, owner=self)
+
+    # pylint: disable=unused-argument
+    def _get_default_input_val(self, val: Any):  # type: ignore
+        # use None value as data placeholder for unfilled inputs.
+        # server side will fill the default value
+        return None
+
+    def _build_inputs_dict(
+        self,
+        inputs: Dict[str, Union[Input, str, bool, int, float]],
+        *,
+        input_definition_dict: Optional[dict] = None,
+    ) -> InputsAttrDict:
+        """Build an input attribute dict so user can get/set inputs by
+        accessing attribute, eg: node1.inputs.xxx.
+
+        :param inputs: Provided kwargs when parameterizing component func.
+        :type inputs: Dict[str, Union[Input, str, bool, int, float]]
+        :keyword input_definition_dict: Static input definition dict. If not provided, will build inputs without meta.
+        :paramtype input_definition_dict: dict
+        :return: Built dynamic input attribute dict.
+        :rtype: InputsAttrDict
+        """
+        if input_definition_dict is not None:
+            # TODO: validate inputs.keys() in input_definitions.keys()
+            input_dict = {}
+            for key, val in input_definition_dict.items():
+                if key in inputs.keys():
+                    # If input is set through component functions' kwargs, create an input object with real value.
+                    data = inputs[key]
+                else:
+                    data = self._get_default_input_val(val)  # pylint: disable=assignment-from-none
+
+                val = self._build_input(name=key, meta=val, data=data)
+                input_dict[key] = val
+        else:
+            input_dict = {key: self._build_input(name=key, meta=None, data=val) for key, val in inputs.items()}
+        return InputsAttrDict(input_dict)
+
+    def _build_outputs_dict(
+        self, outputs: Dict, *, output_definition_dict: Optional[dict] = None, none_data: bool = False
+    ) -> OutputsAttrDict:
+        """Build an output attribute dict so user can get/set outputs by
+        accessing attribute, eg: node1.outputs.xxx.
+
+        :param outputs: Provided kwargs when parameterizing component func.
+        :type outputs: Dict[str, Output]
+        :keyword output_definition_dict: Static output definition dict.
+        :paramtype output_definition_dict: Dict
+        :keyword none_data: If True, will set output data to None.
+        :paramtype none_data: bool
+        :return: Built dynamic output attribute dict.
+        :rtype: OutputsAttrDict
+        """
+        if output_definition_dict is not None:
+            # TODO: check if we need another way to mark a un-configured output instead of just set None.
+            # Create None as data placeholder for all outputs.
+            output_dict = {}
+            for key, val in output_definition_dict.items():
+                if key in outputs.keys():
+                    # If output has given value, create an output object with real value.
+                    val = self._build_output(name=key, meta=val, data=outputs[key])
+                else:
+                    val = self._build_output(name=key, meta=val, data=None)
+                output_dict[key] = val
+        else:
+            output_dict = {}
+            for key, val in outputs.items():
+                output_val = self._build_output(name=key, meta=None, data=val if not none_data else None)
+                output_dict[key] = output_val
+        return OutputsAttrDict(output_dict)
+
+    def _build_inputs(self) -> Dict:
+        """Build inputs of this component to a dict dict which maps output to
+        actual value.
+
+        The built input dict will have same input format as other jobs, eg:
+        {
+           "input_data": Input(path="path/to/input/data", mode="Mount"),
+           "input_value": 10,
+           "learning_rate": "${{jobs.step1.inputs.learning_rate}}"
+        }
+
+        :return: The input dict
+        :rtype: Dict[str, Union[Input, str, bool, int, float]]
+        """
+        inputs = {}
+        # pylint: disable=redefined-builtin
+        for name, input in self.inputs.items():  # type: ignore
+            if isinstance(input, _GroupAttrDict):
+                # Flatten group inputs into inputs dict
+                inputs.update(input.flatten(group_parameter_name=name))
+                continue
+            inputs[name] = input._to_job_input()  # pylint: disable=protected-access
+        return inputs
+
+    def _build_outputs(self) -> Dict[str, Output]:
+        """Build outputs of this component to a dict which maps output to
+        actual value.
+
+        The built output dict will have same output format as other jobs, eg:
+        {
+            "eval_output": "${{jobs.eval.outputs.eval_output}}"
+        }
+
+        :return: The output dict
+        :rtype: Dict[str, Output]
+        """
+        outputs = {}
+        for name, output in self.outputs.items():  # type: ignore
+            if isinstance(output, NodeOutput):
+                output = output._to_job_output()  # pylint: disable=protected-access
+            outputs[name] = output
+        # Remove non-configured output
+        return {k: v for k, v in outputs.items() if v is not None}
+
+    def _to_rest_inputs(self) -> Dict[str, Dict]:
+        """Translate input builders to rest input dicts.
+
+        The built dictionary's format aligns with component job's input yaml, eg:
+        {
+           "input_data": {"data": {"path": "path/to/input/data"},  "mode"="Mount"},
+           "input_value": 10,
+           "learning_rate": "${{jobs.step1.inputs.learning_rate}}"
+        }
+
+        :return: The REST inputs
+        :rtype: Dict[str, Dict]
+        """
+        built_inputs = self._build_inputs()
+        return self._input_entity_to_rest_inputs(input_entity=built_inputs)
+
+    @classmethod
+    def _input_entity_to_rest_inputs(cls, input_entity: Dict[str, Input]) -> Dict[str, Dict]:
+        # Convert io entity to rest io objects
+        input_bindings, dataset_literal_inputs = process_sdk_component_job_io(
+            input_entity, [ComponentJobConstants.INPUT_PATTERN]
+        )
+
+        # parse input_bindings to InputLiteral(value=str(binding))
+        rest_inputs = {**input_bindings, **dataset_literal_inputs}
+        # Note: The function will only be called from BaseNode,
+        # and job_type is used to enable dot in pipeline job input keys,
+        # so pass job_type as None directly here.
+        rest_inputs = to_rest_dataset_literal_inputs(rest_inputs, job_type=None)
+
+        # convert rest io to dict
+        rest_dataset_literal_inputs = {}
+        for name, val in rest_inputs.items():
+            rest_dataset_literal_inputs[name] = val.as_dict()
+            if hasattr(val, "mode") and val.mode:
+                rest_dataset_literal_inputs[name].update({"mode": val.mode.value})
+        return rest_dataset_literal_inputs
+
+    def _to_rest_outputs(self) -> Dict[str, Dict]:
+        """Translate output builders to rest output dicts.
+
+        The built dictionary's format aligns with component job's output yaml, eg:
+        {"eval_output": "${{jobs.eval.outputs.eval_output}}"}
+
+        :return: The REST outputs
+        :rtype: Dict[str, Dict]
+        """
+        built_outputs = self._build_outputs()
+
+        # Convert io entity to rest io objects
+        output_bindings, data_outputs = process_sdk_component_job_io(
+            built_outputs, [ComponentJobConstants.OUTPUT_PATTERN]
+        )
+        rest_data_outputs = to_rest_data_outputs(data_outputs)
+
+        # convert rest io to dict
+        # parse output_bindings to {"value": binding, "type": "literal"} since there's no mode for it
+        rest_output_bindings = {}
+        for key, binding in output_bindings.items():
+            rest_output_bindings[key] = {"value": binding["value"], "type": "literal"}
+            if "mode" in binding:
+                rest_output_bindings[key].update({"mode": binding["mode"].value})
+            if "name" in binding:
+                rest_output_bindings[key].update({"name": binding["name"]})
+            if "version" in binding:
+                rest_output_bindings[key].update({"version": binding["version"]})
+
+        def _rename_name_and_version(output_dict: Dict) -> Dict:
+            # NodeOutput can only be registered with name and version, therefore we rename here
+            if "asset_name" in output_dict.keys():
+                output_dict["name"] = output_dict.pop("asset_name")
+            if "asset_version" in output_dict.keys():
+                output_dict["version"] = output_dict.pop("asset_version")
+            return output_dict
+
+        rest_data_outputs = {name: _rename_name_and_version(val.as_dict()) for name, val in rest_data_outputs.items()}
+        self._update_output_types(rest_data_outputs)
+        rest_data_outputs.update(rest_output_bindings)
+        return rest_data_outputs
+
+    @classmethod
+    def _from_rest_inputs(cls, inputs: Dict) -> Dict[str, Union[Input, str, bool, int, float]]:
+        """Load inputs from rest inputs.
+
+        :param inputs: The REST inputs
+        :type inputs: Dict[str, Union[str, dict]]
+        :return: Input dict
+        :rtype: Dict[str, Union[Input, str, bool, int, float]]
+        """
+
+        # JObject -> RestJobInput/RestJobOutput
+        input_bindings, rest_inputs = from_dict_to_rest_io(inputs, RestJobInput, [ComponentJobConstants.INPUT_PATTERN])
+
+        # RestJobInput/RestJobOutput -> Input/Output
+        dataset_literal_inputs = from_rest_inputs_to_dataset_literal(rest_inputs)
+
+        return {**dataset_literal_inputs, **input_bindings}
+
+    @classmethod
+    def _from_rest_outputs(cls, outputs: Dict[str, Union[str, dict]]) -> Dict:
+        """Load outputs from rest outputs.
+
+        :param outputs: The REST outputs
+        :type outputs: Dict[str, Union[str, dict]]
+        :return: Output dict
+        :rtype: Dict[str, Output]
+        """
+
+        # JObject -> RestJobInput/RestJobOutput
+        output_bindings, rest_outputs = from_dict_to_rest_io(
+            outputs, RestJobOutput, [ComponentJobConstants.OUTPUT_PATTERN]
+        )
+
+        # RestJobInput/RestJobOutput -> Input/Output
+        data_outputs = from_rest_data_outputs(rest_outputs)
+
+        return {**data_outputs, **output_bindings}
+
+    def _update_output_types(self, rest_data_outputs: dict) -> None:
+        """Update output types in rest_data_outputs according to meta level output.
+
+        :param rest_data_outputs: The REST data outputs
+        :type rest_data_outputs: Dict
+        """
+
+        for name, rest_output in rest_data_outputs.items():
+            original_output = self.outputs[name]  # type: ignore
+            # for configured output with meta, "correct" the output type to file to avoid the uri_folder default value
+            if original_output and original_output.type:
+                if original_output.type in ["AnyFile", "uri_file"]:
+                    rest_output["job_output_type"] = "uri_file"
+
+
+def flatten_dict(
+    dct: Optional[Dict],
+    _type: Union[Type["_GroupAttrDict"], Type[GroupInput]],
+    *,
+    allow_dict_fields: Optional[List[str]] = None,
+) -> Dict:
+    """Flatten inputs/input_definitions dict for inputs dict build.
+
+    :param dct: The dictionary to flatten
+    :type dct: Dict
+    :param _type: Either _GroupAttrDict or GroupInput (both have the method `flatten`)
+    :type _type: Union[Type["_GroupAttrDict"], Type[GroupInput]]
+    :keyword allow_dict_fields: A list of keys for dictionary values that will be included in flattened output
+    :paramtype allow_dict_fields: Optional[List[str]]
+    :return: The flattened dict
+    :rtype: Dict
+    """
+    _result = {}
+    if dct is not None:
+        for key, val in dct.items():
+            # to support passing dict value as parameter group
+            if allow_dict_fields and key in allow_dict_fields and isinstance(val, dict):
+                # for child dict, all values are allowed to be dict
+                for flattened_key, flattened_val in flatten_dict(
+                    val, _type, allow_dict_fields=list(val.keys())
+                ).items():
+                    _result[key + "." + flattened_key] = flattened_val
+                continue
+            val = GroupInput.custom_class_value_to_attr_dict(val)
+            if isinstance(val, _type):
+                _result.update(val.flatten(group_parameter_name=key))
+                continue
+            _result[key] = val
+    return _result
+
+
+class NodeWithGroupInputMixin(NodeIOMixin):
+    """This class provide build_inputs_dict for a node to use ParameterGroup as an input."""
+
+    @classmethod
+    def _validate_group_input_type(
+        cls,
+        input_definition_dict: dict,
+        inputs: Dict[str, Union[Input, str, bool, int, float]],
+    ) -> None:
+        """Raise error when group input receive a value not group type.
+
+        :param input_definition_dict: The input definition dict
+        :type input_definition_dict: dict
+        :param inputs: The inputs
+        :type inputs: Dict[str, Union[Input, str, bool, int, float]]
+        """
+        # Note: We put and extra validation here instead of doing it in pipeline._validate()
+        # due to group input will be discarded silently if assign it to a non-group parameter.
+        group_msg = "'%s' is defined as a parameter group but got input '%s' with type '%s'."
+        non_group_msg = "'%s' is defined as a parameter but got a parameter group as input."
+        for key, val in inputs.items():
+            definition = input_definition_dict.get(key)
+            val = GroupInput.custom_class_value_to_attr_dict(val)
+            if val is None:
+                continue
+            # 1. inputs.group = 'a string'
+            if isinstance(definition, GroupInput) and not isinstance(val, (_GroupAttrDict, dict)):
+                raise ValidationException(
+                    message=group_msg % (key, val, type(val)),
+                    no_personal_data_message=group_msg % ("[key]", "[val]", "[type(val)]"),
+                    target=ErrorTarget.PIPELINE,
+                    type=ValidationErrorType.INVALID_VALUE,
+                )
+            # 2. inputs.str_param = group
+            if not isinstance(definition, GroupInput) and isinstance(val, _GroupAttrDict):
+                raise ValidationException(
+                    message=non_group_msg % key,
+                    no_personal_data_message=non_group_msg % "[key]",
+                    target=ErrorTarget.PIPELINE,
+                    type=ValidationErrorType.INVALID_VALUE,
+                )
+
+    @classmethod
+    def _flatten_inputs_and_definition(
+        cls,
+        inputs: Dict[str, Union[Input, str, bool, int, float]],
+        input_definition_dict: dict,
+    ) -> Tuple[Dict, Dict]:
+        """
+        Flatten all GroupInput(definition) and GroupAttrDict recursively and build input dict.
+        For example:
+        input_definition_dict = {
+            "group1": GroupInput(
+                values={
+                    "param1": GroupInput(
+                         values={
+                             "param1_1": Input(type="str"),
+                         }
+                    ),
+                    "param2": Input(type="int"),
+                }
+            ),
+            "group2": GroupInput(
+                values={
+                    "param3": Input(type="str"),
+                }
+            ),
+        } => {
+            "group1.param1.param1_1": Input(type="str"),
+            "group1.param2": Input(type="int"),
+            "group2.param3": Input(type="str"),
+        }
+        inputs = {
+            "group1": {
+                "param1": {
+                    "param1_1": "value1",
+                },
+                "param2": 2,
+            },
+            "group2": {
+                "param3": "value3",
+            },
+        } => {
+            "group1.param1.param1_1": "value1",
+            "group1.param2": 2,
+            "group2.param3": "value3",
+        }
+        :param inputs: The inputs
+        :type inputs: Dict[str, Union[Input, str, bool, int, float]]
+        :param input_definition_dict: The input definition dict
+        :type input_definition_dict: dict
+        :return: The flattened inputs and definition
+        :rtype: Tuple[Dict, Dict]
+        """
+        group_input_names = [key for key, val in input_definition_dict.items() if isinstance(val, GroupInput)]
+        flattened_inputs = flatten_dict(inputs, _GroupAttrDict, allow_dict_fields=group_input_names)
+        flattened_definition_dict = flatten_dict(input_definition_dict, GroupInput)
+        return flattened_inputs, flattened_definition_dict
+
+    def _build_inputs_dict(
+        self,
+        inputs: Dict[str, Union[Input, str, bool, int, float]],
+        *,
+        input_definition_dict: Optional[dict] = None,
+    ) -> InputsAttrDict:
+        """Build an input attribute dict so user can get/set inputs by
+        accessing attribute, eg: node1.inputs.xxx.
+
+        :param inputs: Provided kwargs when parameterizing component func.
+        :type inputs: Dict[str, Union[Input, str, bool, int, float]]
+        :keyword input_definition_dict: Input definition dict from component entity.
+        :paramtype input_definition_dict: dict
+        :return: Built input attribute dict.
+        :rtype: InputsAttrDict
+        """
+
+        # TODO: should we support group input when there is no local input definition?
+        if input_definition_dict is not None:
+            # Validate group mismatch
+            self._validate_group_input_type(input_definition_dict, inputs)
+
+            # Flatten inputs and definition
+            flattened_inputs, flattened_definition_dict = self._flatten_inputs_and_definition(
+                inputs, input_definition_dict
+            )
+            # Build: zip all flattened parameter with definition
+            inputs = super()._build_inputs_dict(flattened_inputs, input_definition_dict=flattened_definition_dict)
+            return InputsAttrDict(GroupInput.restore_flattened_inputs(inputs))
+        return super()._build_inputs_dict(inputs)
+
+
+class PipelineJobIOMixin(NodeWithGroupInputMixin):
+    """Provides ability to wrap pipeline job inputs/outputs and build data bindings
+    dynamically."""
+
+    def _build_input(self, name: str, meta: Optional[Input], data: Any) -> "PipelineInput":
+        return PipelineInput(name=name, meta=meta, data=data, owner=self)
+
+    def _build_output(
+        self, name: str, meta: Optional[Union[Input, Output]], data: Optional[Union[Output, str]]
+    ) -> "PipelineOutput":
+        # TODO: settings data to None for un-configured outputs so we won't passing extra fields(eg: default mode)
+        result = PipelineOutput(port_name=name, meta=meta, data=data, owner=self)
+        return result
+
+    def _build_inputs_dict(
+        self,
+        inputs: Dict[str, Union[Input, str, bool, int, float]],
+        *,
+        input_definition_dict: Optional[dict] = None,
+    ) -> InputsAttrDict:
+        """Build an input attribute dict so user can get/set inputs by
+        accessing attribute, eg: node1.inputs.xxx.
+
+        :param inputs: Provided kwargs when parameterizing component func.
+        :type inputs: Dict[str, Union[Input, str, bool, int, float]]
+        :keyword input_definition_dict: Input definition dict from component entity.
+        :return: Built input attribute dict.
+        :rtype: InputsAttrDict
+        """
+        input_dict = super()._build_inputs_dict(inputs, input_definition_dict=input_definition_dict)
+        # TODO: should we do this when input_definition_dict is not None?
+        # TODO: should we put this in super()._build_inputs_dict?
+        if input_definition_dict is None:
+            return InputsAttrDict(GroupInput.restore_flattened_inputs(input_dict))
+        return input_dict
+
+    def _build_output_for_pipeline(self, name: str, data: Optional[Union[Output, NodeOutput]]) -> "PipelineOutput":
+        """Build an output object for pipeline and copy settings from source output.
+
+        :param name: Output name.
+        :type name: str
+        :param data: Output data.
+        :type data: Optional[Union[Output, NodeOutput]]
+        :return: Built output object.
+        :rtype: PipelineOutput
+        """
+        # pylint: disable=protected-access
+        if data is None:
+            # For None output, build an empty output builder
+            output_val = self._build_output(name=name, meta=None, data=None)
+        elif isinstance(data, Output):
+            # For output entity, build an output builder with data points to it
+            output_val = self._build_output(name=name, meta=data, data=data)
+        elif isinstance(data, NodeOutput):
+            # For output builder, build a new output builder and copy settings from it
+            output_val = self._build_output(name=name, meta=data._meta, data=None)
+            copy_output_setting(source=data, target=output_val)
+        else:
+            message = "Unsupported output type: {} for pipeline output: {}: {}"
+            raise ValidationException(
+                message=message.format(type(data), name, data),
+                no_personal_data_message=message,
+                target=ErrorTarget.PIPELINE,
+            )
+        return output_val
+
+    def _build_pipeline_outputs_dict(self, outputs: Dict) -> OutputsAttrDict:
+        """Build an output attribute dict without output definition metadata.
+        For pipeline outputs, its setting should be copied from node level outputs.
+
+        :param outputs: Node output dict or pipeline component's outputs.
+        :type outputs: Dict[str, Union[Output, NodeOutput]]
+        :return: Built dynamic output attribute dict.
+        :rtype: OutputsAttrDict
+        """
+        output_dict = {}
+        for key, val in outputs.items():
+            output_dict[key] = self._build_output_for_pipeline(name=key, data=val)
+        return OutputsAttrDict(output_dict)
+
+    def _build_outputs(self) -> Dict[str, Output]:
+        """Build outputs of this pipeline to a dict which maps output to actual
+        value.
+
+        The built dictionary's format aligns with component job's output yaml,
+        un-configured outputs will be None, eg:
+        {"eval_output": "${{jobs.eval.outputs.eval_output}}", "un_configured": None}
+
+        :return: The output dict
+        :rtype: Dict[str, Output]
+        """
+        outputs = {}
+        for name, output in self.outputs.items():  # type: ignore
+            if isinstance(output, NodeOutput):
+                output = output._to_job_output()  # pylint: disable=protected-access
+            outputs[name] = output
+        return outputs
+
+    def _get_default_input_val(self, val: Any):  # type: ignore
+        # use Default value as data placeholder for unfilled inputs.
+        # client side need to fill the default value for dsl.pipeline
+        if isinstance(val, GroupInput):
+            # Copy default value dict for group
+            return copy.deepcopy(val.default)
+        return val.default
+
+    def _update_output_types(self, rest_data_outputs: Dict) -> None:
+        """Won't clear output type for pipeline level outputs since it's required in rest object.
+
+        :param rest_data_outputs: The REST data outputs
+        :type rest_data_outputs: Dict
+        """
+
+
+class AutoMLNodeIOMixin(NodeIOMixin):
+    """Wrap outputs of automl node and build data bindings dynamically."""
+
+    def __init__(self, **kwargs):  # type: ignore
+        # add a inputs field to align with other nodes
+        self.inputs = {}
+        super(AutoMLNodeIOMixin, self).__init__(**kwargs)
+        if getattr(self, "outputs", None):
+            self._outputs = self._build_outputs_dict(self.outputs or {})
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_load_component.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_load_component.py
new file mode 100644
index 00000000..60c4cbe7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_load_component.py
@@ -0,0 +1,313 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+from typing import Any, Callable, Dict, List, Mapping, Optional, Union, cast
+
+from marshmallow import INCLUDE
+
+from azure.ai.ml import Output
+from azure.ai.ml._schema import NestedField
+from azure.ai.ml._schema.pipeline.component_job import SweepSchema
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, SOURCE_PATH_CONTEXT_KEY, CommonYamlFields
+from azure.ai.ml.constants._component import ControlFlowType, DataTransferTaskType, NodeType
+from azure.ai.ml.constants._compute import ComputeType
+from azure.ai.ml.dsl._component_func import to_component_func
+from azure.ai.ml.dsl._overrides_definition import OverrideDefinition
+from azure.ai.ml.entities._builders import (
+    BaseNode,
+    Command,
+    DataTransferCopy,
+    DataTransferExport,
+    DataTransferImport,
+    Import,
+    Parallel,
+    Spark,
+    Sweep,
+)
+from azure.ai.ml.entities._builders.condition_node import ConditionNode
+from azure.ai.ml.entities._builders.control_flow_node import ControlFlowNode
+from azure.ai.ml.entities._builders.do_while import DoWhile
+from azure.ai.ml.entities._builders.parallel_for import ParallelFor
+from azure.ai.ml.entities._builders.pipeline import Pipeline
+from azure.ai.ml.entities._component.component import Component
+from azure.ai.ml.entities._job.automl.automl_job import AutoMLJob
+from azure.ai.ml.entities._util import get_type_from_spec
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+class _PipelineNodeFactory:
+    """A class to create pipeline node instances from yaml dict or rest objects without hard-coded type check."""
+
+    def __init__(self) -> None:
+        self._create_instance_funcs: dict = {}
+        self._load_from_rest_object_funcs: dict = {}
+
+        self.register_type(
+            _type=NodeType.COMMAND,
+            create_instance_func=lambda: Command.__new__(Command),
+            load_from_rest_object_func=Command._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type=NodeType.IMPORT,
+            create_instance_func=lambda: Import.__new__(Import),
+            load_from_rest_object_func=Import._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type=NodeType.PARALLEL,
+            create_instance_func=lambda: Parallel.__new__(Parallel),
+            load_from_rest_object_func=Parallel._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type=NodeType.PIPELINE,
+            create_instance_func=lambda: Pipeline.__new__(Pipeline),
+            load_from_rest_object_func=Pipeline._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type=NodeType.SWEEP,
+            create_instance_func=lambda: Sweep.__new__(Sweep),
+            load_from_rest_object_func=Sweep._from_rest_object,
+            nested_schema=NestedField(SweepSchema, unknown=INCLUDE),
+        )
+        self.register_type(
+            _type=NodeType.AUTOML,
+            create_instance_func=None,
+            load_from_rest_object_func=self._automl_from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type=NodeType.SPARK,
+            create_instance_func=lambda: Spark.__new__(Spark),
+            load_from_rest_object_func=Spark._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type=ControlFlowType.DO_WHILE,
+            create_instance_func=None,
+            load_from_rest_object_func=DoWhile._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type=ControlFlowType.IF_ELSE,
+            create_instance_func=None,
+            load_from_rest_object_func=ConditionNode._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type=ControlFlowType.PARALLEL_FOR,
+            create_instance_func=None,
+            load_from_rest_object_func=ParallelFor._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type="_".join([NodeType.DATA_TRANSFER, DataTransferTaskType.COPY_DATA]),
+            create_instance_func=lambda: DataTransferCopy.__new__(DataTransferCopy),
+            load_from_rest_object_func=DataTransferCopy._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type="_".join([NodeType.DATA_TRANSFER, DataTransferTaskType.IMPORT_DATA]),
+            create_instance_func=lambda: DataTransferImport.__new__(DataTransferImport),
+            load_from_rest_object_func=DataTransferImport._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type="_".join([NodeType.DATA_TRANSFER, DataTransferTaskType.EXPORT_DATA]),
+            create_instance_func=lambda: DataTransferExport.__new__(DataTransferExport),
+            load_from_rest_object_func=DataTransferExport._from_rest_object,
+            nested_schema=None,
+        )
+        self.register_type(
+            _type=NodeType.FLOW_PARALLEL,
+            create_instance_func=lambda: Parallel.__new__(Parallel),
+            load_from_rest_object_func=None,
+            nested_schema=None,
+        )
+
+    @classmethod
+    def _get_func(cls, _type: str, funcs: Dict[str, Callable]) -> Callable:
+        if _type == NodeType._CONTAINER:
+            msg = (
+                "Component returned by 'list' is abbreviated and can not be used directly, "
+                "please use result from 'get'."
+            )
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.COMPONENT,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        _type = get_type_from_spec({CommonYamlFields.TYPE: _type}, valid_keys=funcs)
+        return funcs[_type]
+
+    def get_create_instance_func(self, _type: str) -> Callable[..., BaseNode]:
+        """Get the function to create a new instance of the node.
+
+        :param _type: The type of the node.
+        :type _type: str
+        :return: The create instance function
+        :rtype: Callable[..., BaseNode]
+        """
+        return self._get_func(_type, self._create_instance_funcs)
+
+    def get_load_from_rest_object_func(self, _type: str) -> Callable:
+        """Get the function to load a node from a rest object.
+
+        :param _type: The type of the node.
+        :type _type: str
+        :return: The `_load_from_rest_object` function
+        :rtype: Callable[[Any], Union[BaseNode, AutoMLJob, ControlFlowNode]]
+        """
+        return self._get_func(_type, self._load_from_rest_object_funcs)
+
+    def register_type(
+        self,
+        _type: str,
+        *,
+        create_instance_func: Optional[Callable[..., Union[BaseNode, AutoMLJob]]] = None,
+        load_from_rest_object_func: Optional[Callable] = None,
+        nested_schema: Optional[Union[NestedField, List[NestedField]]] = None,
+    ) -> None:
+        """Register a type of node.
+
+        :param _type: The type of the node.
+        :type _type: str
+        :keyword create_instance_func: A function to create a new instance of the node
+        :paramtype create_instance_func: typing.Optional[typing.Callable[..., typing.Union[BaseNode, AutoMLJob]]]
+        :keyword load_from_rest_object_func: A function to load a node from a rest object
+        :paramtype load_from_rest_object_func: typing.Optional[typing.Callable[[Any], typing.Union[BaseNode, AutoMLJob\
+            , ControlFlowNode]]]
+        :keyword nested_schema: schema/schemas of corresponding nested field, will be used in \
+            PipelineJobSchema.jobs.value
+        :paramtype nested_schema: typing.Optional[typing.Union[NestedField, List[NestedField]]]
+        """
+        if create_instance_func is not None:
+            self._create_instance_funcs[_type] = create_instance_func
+        if load_from_rest_object_func is not None:
+            self._load_from_rest_object_funcs[_type] = load_from_rest_object_func
+        if nested_schema is not None:
+            from azure.ai.ml._schema.core.fields import TypeSensitiveUnionField
+            from azure.ai.ml._schema.pipeline.pipeline_component import PipelineComponentSchema
+            from azure.ai.ml._schema.pipeline.pipeline_job import PipelineJobSchema
+
+            for declared_fields in [
+                PipelineJobSchema._declared_fields,
+                PipelineComponentSchema._declared_fields,
+            ]:
+                jobs_value_field: TypeSensitiveUnionField = declared_fields["jobs"].value_field
+                if not isinstance(nested_schema, list):
+                    nested_schema = [nested_schema]
+                for nested_field in nested_schema:
+                    jobs_value_field.insert_type_sensitive_field(type_name=_type, field=nested_field)
+
+    def load_from_dict(self, *, data: dict, _type: Optional[str] = None) -> Union[BaseNode, AutoMLJob]:
+        """Load a node from a dict.
+
+        :keyword data: A dict containing the node's data.
+        :paramtype data: dict
+        :keyword _type: The type of the node. If not specified, it will be inferred from the data.
+        :paramtype _type: str
+        :return: The node
+        :rtype: Union[BaseNode, AutoMLJob]
+        """
+        if _type is None:
+            _type = data[CommonYamlFields.TYPE] if CommonYamlFields.TYPE in data else NodeType.COMMAND
+            # todo: refine Hard code for now to support different task type for DataTransfer node
+            if _type == NodeType.DATA_TRANSFER:
+                _type = "_".join([NodeType.DATA_TRANSFER, data.get("task", " ")])
+        else:
+            data[CommonYamlFields.TYPE] = _type
+
+        new_instance: Union[BaseNode, AutoMLJob] = self.get_create_instance_func(_type)()
+
+        if isinstance(new_instance, BaseNode):
+            # parse component
+            component_key = new_instance._get_component_attr_name()
+            if component_key in data and isinstance(data[component_key], dict):
+                data[component_key] = Component._load(
+                    data=data[component_key],
+                    yaml_path=data[component_key].pop(SOURCE_PATH_CONTEXT_KEY, None),
+                )
+        # TODO: Bug Item number: 2883415
+        new_instance.__init__(**data)  # type: ignore
+        return new_instance
+
+    def load_from_rest_object(
+        self, *, obj: dict, _type: Optional[str] = None, **kwargs: Any
+    ) -> Union[BaseNode, AutoMLJob, ControlFlowNode]:
+        """Load a node from a rest object.
+
+        :keyword obj: A rest object containing the node's data.
+        :paramtype obj: dict
+        :keyword _type: The type of the node. If not specified, it will be inferred from the data.
+        :paramtype _type: str
+        :return: The node
+        :rtype: Union[BaseNode, AutoMLJob, ControlFlowNode]
+        """
+
+        # TODO: Remove in PuP with native import job/component type support in MFE/Designer
+        if "computeId" in obj and obj["computeId"] and obj["computeId"].endswith("/" + ComputeType.ADF):
+            _type = NodeType.IMPORT
+
+        if _type is None:
+            _type = obj[CommonYamlFields.TYPE] if CommonYamlFields.TYPE in obj else NodeType.COMMAND
+            # todo: refine Hard code for now to support different task type for DataTransfer node
+            if _type == NodeType.DATA_TRANSFER:
+                _type = "_".join([NodeType.DATA_TRANSFER, obj.get("task", " ")])
+        else:
+            obj[CommonYamlFields.TYPE] = _type
+
+        res: Union[BaseNode, AutoMLJob, ControlFlowNode] = self.get_load_from_rest_object_func(_type)(obj, **kwargs)
+        return res
+
+    @classmethod
+    def _automl_from_rest_object(cls, node: Dict) -> AutoMLJob:
+        _outputs = cast(Dict[str, Union[str, dict]], node.get("outputs"))
+        # rest dict outputs -> Output objects
+        outputs = AutoMLJob._from_rest_outputs(_outputs)
+        # Output objects -> yaml dict outputs
+        parsed_outputs = {}
+        for key, val in outputs.items():
+            if isinstance(val, Output):
+                val = val._to_dict()
+            parsed_outputs[key] = val
+        node["outputs"] = parsed_outputs
+        return AutoMLJob._load_from_dict(
+            node,
+            context={BASE_PATH_CONTEXT_KEY: "./"},
+            additional_message="Failed to load automl task from backend.",
+            inside_pipeline=True,
+        )
+
+
+def _generate_component_function(
+    component_entity: Component,
+    override_definitions: Optional[Mapping[str, OverrideDefinition]] = None,  # pylint: disable=unused-argument
+) -> Callable[..., Union[Command, Parallel]]:
+    # Generate a function which returns a component node.
+    def create_component_func(**kwargs: Any) -> Union[BaseNode, AutoMLJob]:
+        # todo: refine Hard code for now to support different task type for DataTransfer node
+        _type = component_entity.type
+        if _type == NodeType.DATA_TRANSFER:
+            # TODO: Bug Item number: 2883431
+            _type = "_".join([NodeType.DATA_TRANSFER, component_entity.task])  # type: ignore
+            if component_entity.task == DataTransferTaskType.IMPORT_DATA:  # type: ignore
+                return pipeline_node_factory.load_from_dict(
+                    data={"component": component_entity, "_from_component_func": True, **kwargs},
+                    _type=_type,
+                )
+        return pipeline_node_factory.load_from_dict(
+            data={"component": component_entity, "inputs": kwargs, "_from_component_func": True},
+            _type=_type,
+        )
+
+    res: Callable = to_component_func(component_entity, create_component_func)
+    return res
+
+
+pipeline_node_factory = _PipelineNodeFactory()
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_pipeline_expression.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_pipeline_expression.py
new file mode 100644
index 00000000..49bb8a61
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_pipeline_expression.py
@@ -0,0 +1,662 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import re
+import tempfile
+from collections import namedtuple
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Union, cast
+
+from azure.ai.ml._utils.utils import dump_yaml_to_file, get_all_data_binding_expressions, load_yaml
+from azure.ai.ml.constants._common import AZUREML_PRIVATE_FEATURES_ENV_VAR, DefaultOpenEncoding
+from azure.ai.ml.constants._component import ComponentParameterTypes, IOConstants
+from azure.ai.ml.exceptions import UserErrorException
+
+if TYPE_CHECKING:
+    from azure.ai.ml.entities._builders import BaseNode
+
+ExpressionInput = namedtuple("ExpressionInput", ["name", "type", "value"])
+NONE_PARAMETER_TYPE = "None"
+
+
+class PipelineExpressionOperator:
+    """Support operator in native Python experience."""
+
+    ADD = "+"
+    SUB = "-"
+    MUL = "*"
+    DIV = "/"
+    MOD = "%"
+    POW = "**"
+    FLOORDIV = "//"
+    LT = "<"
+    GT = ">"
+    LTE = "<="
+    GTE = ">="
+    EQ = "=="
+    NE = "!="
+    AND = "&"
+    OR = "|"
+    XOR = "^"
+
+
+_SUPPORTED_OPERATORS = {
+    getattr(PipelineExpressionOperator, attr)
+    for attr in PipelineExpressionOperator.__dict__
+    if not attr.startswith("__")
+}
+
+
+def _enumerate_operation_combination() -> Dict[str, Union[str, Exception]]:
+    """Enumerate the result type of binary operations on types
+
+    Leverages `eval` to validate operation and get its result type.
+
+    :return: A dictionary that maps an operation to either:
+      * A result type
+      * An Exception
+    :rtype: Dict[str, Union[str, Exception]]
+    """
+    res: Dict = {}
+    primitive_types_values = {
+        NONE_PARAMETER_TYPE: repr(None),
+        ComponentParameterTypes.BOOLEAN: repr(True),
+        ComponentParameterTypes.INTEGER: repr(1),
+        ComponentParameterTypes.NUMBER: repr(1.0),
+        ComponentParameterTypes.STRING: repr("1"),
+    }
+    for type1, operand1 in primitive_types_values.items():
+        for type2, operand2 in primitive_types_values.items():
+            for operator in _SUPPORTED_OPERATORS:
+                k = f"{type1} {operator} {type2}"
+                try:
+                    eval_result = eval(f"{operand1} {operator} {operand2}")  # pylint: disable=eval-used # nosec
+                    res[k] = IOConstants.PRIMITIVE_TYPE_2_STR[type(eval_result)]
+                except TypeError:
+                    error_message = (
+                        f"Operator '{operator}' is not supported between instances of '{type1}' and '{type2}'."
+                    )
+                    res[k] = UserErrorException(message=error_message, no_personal_data_message=error_message)
+    return res
+
+
+# enumerate and store as a lookup table:
+#   key format is "<operand1_type> <operator> <operand2_type>"
+#   value can be either result type as str and UserErrorException for invalid operation
+_OPERATION_RESULT_TYPE_LOOKUP = _enumerate_operation_combination()
+
+
+class PipelineExpressionMixin:
+    _SUPPORTED_PRIMITIVE_TYPES = (bool, int, float, str)
+    _SUPPORTED_PIPELINE_INPUT_TYPES = (
+        ComponentParameterTypes.BOOLEAN,
+        ComponentParameterTypes.INTEGER,
+        ComponentParameterTypes.NUMBER,
+        ComponentParameterTypes.STRING,
+    )
+
+    def _validate_binary_operation(self, other: Any, operator: str) -> None:
+        from azure.ai.ml.entities._job.pipeline._io import NodeOutput, PipelineInput
+
+        if (
+            other is not None
+            and not isinstance(other, self._SUPPORTED_PRIMITIVE_TYPES)
+            and not isinstance(other, (PipelineInput, NodeOutput, PipelineExpression))
+        ):
+            error_message = (
+                f"Operator '{operator}' is not supported with {type(other)}; "
+                "currently only support primitive types (None, bool, int, float and str), "
+                "pipeline input, component output and expression."
+            )
+            raise UserErrorException(message=error_message, no_personal_data_message=error_message)
+
+    def __add__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.ADD)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.ADD)
+
+    def __radd__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.ADD)
+        return PipelineExpression._from_operation(other, self, PipelineExpressionOperator.ADD)
+
+    def __sub__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.SUB)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.SUB)
+
+    def __rsub__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.SUB)
+        return PipelineExpression._from_operation(other, self, PipelineExpressionOperator.SUB)
+
+    def __mul__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.MUL)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.MUL)
+
+    def __rmul__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.MUL)
+        return PipelineExpression._from_operation(other, self, PipelineExpressionOperator.MUL)
+
+    def __truediv__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.DIV)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.DIV)
+
+    def __rtruediv__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.DIV)
+        return PipelineExpression._from_operation(other, self, PipelineExpressionOperator.DIV)
+
+    def __mod__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.MOD)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.MOD)
+
+    def __rmod__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.MOD)
+        return PipelineExpression._from_operation(other, self, PipelineExpressionOperator.MOD)
+
+    def __pow__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.POW)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.POW)
+
+    def __rpow__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.POW)
+        return PipelineExpression._from_operation(other, self, PipelineExpressionOperator.POW)
+
+    def __floordiv__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.FLOORDIV)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.FLOORDIV)
+
+    def __rfloordiv__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.FLOORDIV)
+        return PipelineExpression._from_operation(other, self, PipelineExpressionOperator.FLOORDIV)
+
+    def __lt__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.LT)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.LT)
+
+    def __gt__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.GT)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.GT)
+
+    def __le__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.LTE)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.LTE)
+
+    def __ge__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.GTE)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.GTE)
+
+    # TODO: Bug Item number: 2883354
+    def __eq__(self, other: Any) -> "PipelineExpression":  # type: ignore
+        self._validate_binary_operation(other, PipelineExpressionOperator.EQ)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.EQ)
+
+    # TODO: Bug Item number: 2883354
+    def __ne__(self, other: Any) -> "PipelineExpression":  # type: ignore
+        self._validate_binary_operation(other, PipelineExpressionOperator.NE)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.NE)
+
+    def __and__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.AND)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.AND)
+
+    def __or__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.OR)
+        return PipelineExpression._from_operation(self, other, PipelineExpressionOperator.OR)
+
+    def __xor__(self, other: Any) -> "PipelineExpression":
+        self._validate_binary_operation(other, PipelineExpressionOperator.XOR)
+        return PipelineExpression._from_operation(self, None, PipelineExpressionOperator.XOR)
+
+    def __bool__(self) -> bool:
+        """Python method that is used to implement truth value testing and the built-in operation bool().
+
+        This method is not supported as PipelineExpressionMixin is designed to record operation history,
+        while this method can only return False or True, leading to history breaks here.
+        As overloadable boolean operators PEP (refer to: https://www.python.org/dev/peps/pep-0335/)
+        was rejected, logical operations are also not supported.
+
+        :return: True if not inside dsl pipeline func, raises otherwise
+        :rtype: bool
+        """
+        from azure.ai.ml.dsl._pipeline_component_builder import _is_inside_dsl_pipeline_func
+
+        # note: unexpected bool test always be checking if the object is None;
+        # so for non-pipeline scenarios, directly return True to avoid unexpected breaking,
+        # and for pipeline scenarios, will use is not None to replace bool test.
+        if not _is_inside_dsl_pipeline_func():
+            return True
+
+        error_message = f"Type {type(self)} is not supported for operation bool()."
+        raise UserErrorException(message=error_message, no_personal_data_message=error_message)
+
+
+class PipelineExpression(PipelineExpressionMixin):
+    """Pipeline expression entity.
+
+    Use PipelineExpression to support simple and trivial parameter transformation tasks with constants
+    or other parameters. Operations are recorded in this class during executions, and expected result
+    will be generated for corresponding scenario.
+    """
+
+    _PIPELINE_INPUT_PREFIX = ["parent", "inputs"]
+    _PIPELINE_INPUT_PATTERN = re.compile(pattern=r"parent.inputs.(?P<pipeline_input_name>[^.]+)")
+    _PIPELINE_INPUT_NAME_GROUP = "pipeline_input_name"
+    # AML type to Python type, for generated Python code
+    _TO_PYTHON_TYPE = {
+        ComponentParameterTypes.BOOLEAN: bool.__name__,
+        ComponentParameterTypes.INTEGER: int.__name__,
+        ComponentParameterTypes.NUMBER: float.__name__,
+        ComponentParameterTypes.STRING: str.__name__,
+    }
+
+    _INDENTATION = "    "
+    _IMPORT_MLDESIGNER_LINE = "from mldesigner import command_component, Output"
+    _DECORATOR_LINE = "@command_component(@@decorator_parameters@@)"
+    _COMPONENT_FUNC_NAME = "expression_func"
+    _COMPONENT_FUNC_DECLARATION_LINE = (
+        f"def {_COMPONENT_FUNC_NAME}(@@component_parameters@@)" " -> Output(type=@@return_type@@):"
+    )
+    _PYTHON_CACHE_FOLDER_NAME = "__pycache__"
+
+    def __init__(self, postfix: List[str], inputs: Dict[str, ExpressionInput]):
+        self._postfix = postfix
+        self._inputs = inputs.copy()  # including PiplineInput and Output, extra stored name and type
+        self._result_type: Optional[str] = None
+        self._created_component = None
+
+    @property
+    def expression(self) -> str:
+        """Infix expression string, wrapped with parentheses.
+
+        :return: The infix expression
+        :rtype: str
+        """
+        return self._to_infix()
+
+    def __str__(self) -> str:
+        return self._to_data_binding()
+
+    def _data_binding(self) -> str:
+        return self._to_data_binding()
+
+    def _to_infix(self) -> str:
+        stack = []
+        for token in self._postfix:
+            if token not in _SUPPORTED_OPERATORS:
+                stack.append(token)
+                continue
+            operand2, operand1 = stack.pop(), stack.pop()
+            stack.append(f"({operand1} {token} {operand2})")
+        return stack.pop()
+
+    # pylint: disable=too-many-statements
+    @staticmethod
+    def _handle_operand(
+        operand: "PipelineExpression",
+        postfix: List[str],
+        expression_inputs: Dict[str, ExpressionInput],
+        pipeline_inputs: dict,
+    ) -> Tuple[List[str], Dict[str, ExpressionInput]]:
+        """Handle operand in expression, update postfix expression and expression inputs.
+
+        :param operand: The operand
+        :type operand: "PipelineExpression"
+        :param postfix:
+        :type postfix: List[str]
+        :param expression_inputs: The expression inputs
+        :type expression_inputs: Dict[str, ExpressionInput]
+        :param pipeline_inputs: The pipeline inputs
+        :type pipeline_inputs: dict
+        :return: A 2-tuple of the updated postfix expression and expression inputs
+        :rtype: Tuple[List[str], Dict[str, ExpressionInput]]
+        """
+        from azure.ai.ml.entities._job.pipeline._io import NodeOutput, PipelineInput
+
+        def _update_postfix(_postfix: List[str], _old_name: str, _new_name: str) -> List[str]:
+            return list(map(lambda _x: _new_name if _x == _old_name else _x, _postfix))
+
+        def _get_or_create_input_name(
+            _original_name: str,
+            _operand: Union[PipelineInput, NodeOutput],
+            _expression_inputs: Dict[str, ExpressionInput],
+        ) -> str:
+            """Get or create expression input name as current operand may have appeared in expression.
+
+            :param _original_name: The original name
+            :type _original_name: str
+            :param _operand: The expression operand
+            :type _operand: Union[PipelineInput, NodeOutput]
+            :param _expression_inputs: The expression inputs
+            :type _expression_inputs: Dict[str, ExpressionInput]
+            :return: The input name
+            :rtype: str
+            """
+            _existing_id_to_name = {id(_v.value): _k for _k, _v in _expression_inputs.items()}
+            if id(_operand) in _existing_id_to_name:
+                return _existing_id_to_name[id(_operand)]
+            # use a counter to generate a unique name for current operand
+            _name, _counter = _original_name, 0
+            while _name in _expression_inputs:
+                _name = f"{_original_name}_{_counter}"
+                _counter += 1
+            return _name
+
+        def _handle_pipeline_input(
+            _pipeline_input: PipelineInput,
+            _postfix: List[str],
+            _expression_inputs: Dict[str, ExpressionInput],
+        ) -> Tuple[List[str], dict]:
+            _name = _pipeline_input._port_name
+            # 1. use name with counter for pipeline input; 2. add component's name to component output
+            if _name in _expression_inputs:
+                _seen_input = _expression_inputs[_name]
+                if isinstance(_seen_input.value, PipelineInput):
+                    _name = _get_or_create_input_name(_name, _pipeline_input, _expression_inputs)
+                else:
+                    _expression_inputs.pop(_name)
+                    _new_name = f"{_seen_input.value._owner.component.name}__{_seen_input.value._port_name}"
+                    _postfix = _update_postfix(_postfix, _name, _new_name)
+                    _expression_inputs[_new_name] = ExpressionInput(_new_name, _seen_input.type, _seen_input)
+            _postfix.append(_name)
+
+            param_input = pipeline_inputs
+            for group_name in _pipeline_input._group_names:
+                param_input = param_input[group_name].values
+            _expression_inputs[_name] = ExpressionInput(
+                _name, param_input[_pipeline_input._port_name].type, _pipeline_input
+            )
+            return _postfix, _expression_inputs
+
+        def _handle_component_output(
+            _component_output: NodeOutput,
+            _postfix: List[str],
+            _expression_inputs: Dict[str, ExpressionInput],
+        ) -> Tuple[List[str], dict]:
+            if _component_output._meta is not None and not _component_output._meta._is_primitive_type:
+                error_message = (
+                    f"Component output {_component_output._port_name} in expression must "
+                    f"be a primitive type with value {True!r}, "
+                    f"got {_component_output._meta._is_primitive_type!r}"
+                )
+                raise UserErrorException(message=error_message, no_personal_data_message=error_message)
+            _name = _component_output._port_name
+            _has_prefix = False
+            # "output" is the default output name for command component, add component's name as prefix
+            if _name == "output":
+                if _component_output._owner is not None and not isinstance(_component_output._owner.component, str):
+                    _name = f"{_component_output._owner.component.name}__output"
+                _has_prefix = True
+            # following loop is expected to execute at most twice:
+            #   1. add component's name to output(s)
+            #   2. use name with counter
+            while _name in _expression_inputs:
+                _seen_input = _expression_inputs[_name]
+                if isinstance(_seen_input.value, PipelineInput):
+                    if not _has_prefix:
+                        if _component_output._owner is not None and not isinstance(
+                            _component_output._owner.component, str
+                        ):
+                            _name = f"{_component_output._owner.component.name}__{_component_output._port_name}"
+                        _has_prefix = True
+                        continue
+                    _name = _get_or_create_input_name(_name, _component_output, _expression_inputs)
+                else:
+                    if not _has_prefix:
+                        _expression_inputs.pop(_name)
+                        _new_name = f"{_seen_input.value._owner.component.name}__{_seen_input.value._port_name}"
+                        _postfix = _update_postfix(_postfix, _name, _new_name)
+                        _expression_inputs[_new_name] = ExpressionInput(_new_name, _seen_input.type, _seen_input)
+                        if _component_output._owner is not None and not isinstance(
+                            _component_output._owner.component, str
+                        ):
+                            _name = f"{_component_output._owner.component.name}__{_component_output._port_name}"
+                        _has_prefix = True
+                    _name = _get_or_create_input_name(_name, _component_output, _expression_inputs)
+            _postfix.append(_name)
+            _expression_inputs[_name] = ExpressionInput(_name, _component_output.type, _component_output)
+            return _postfix, _expression_inputs
+
+        if operand is None or isinstance(operand, PipelineExpression._SUPPORTED_PRIMITIVE_TYPES):
+            postfix.append(repr(operand))
+        elif isinstance(operand, PipelineInput):
+            postfix, expression_inputs = _handle_pipeline_input(operand, postfix, expression_inputs)
+        elif isinstance(operand, NodeOutput):
+            postfix, expression_inputs = _handle_component_output(operand, postfix, expression_inputs)
+        elif isinstance(operand, PipelineExpression):
+            postfix.extend(operand._postfix.copy())
+            expression_inputs.update(operand._inputs.copy())
+        return postfix, expression_inputs
+
+    @staticmethod
+    def _from_operation(operand1: Any, operand2: Any, operator: str) -> "PipelineExpression":
+        if operator not in _SUPPORTED_OPERATORS:
+            error_message = (
+                f"Operator '{operator}' is not supported operator, "
+                f"currently supported operators are {','.join(_SUPPORTED_OPERATORS)}."
+            )
+            raise UserErrorException(message=error_message, no_personal_data_message=error_message)
+
+        # get all pipeline input types from builder stack
+        # TODO: check if there is pipeline input we cannot know its type (missing in `PipelineComponentBuilder.inputs`)?
+        from azure.ai.ml.dsl._pipeline_component_builder import _definition_builder_stack
+
+        res = _definition_builder_stack.top()
+        pipeline_inputs = res.inputs if res is not None else {}
+        postfix: List[str] = []
+        inputs: Dict[str, ExpressionInput] = {}
+        postfix, inputs = PipelineExpression._handle_operand(operand1, postfix, inputs, pipeline_inputs)
+        postfix, inputs = PipelineExpression._handle_operand(operand2, postfix, inputs, pipeline_inputs)
+        postfix.append(operator)
+        return PipelineExpression(postfix, inputs)
+
+    @property
+    def _string_concatenation(self) -> bool:
+        """If all operands are string and operations are addition, it is a string concatenation expression.
+
+        :return: Whether this represents string concatenation
+        :rtype: bool
+        """
+        for token in self._postfix:
+            # operator can only be "+" for string concatenation
+            if token in _SUPPORTED_OPERATORS:
+                if token != PipelineExpressionOperator.ADD:
+                    return False
+                continue
+            # constant and PiplineInput should be type string
+            if token in self._inputs:
+                if self._inputs[token].type != ComponentParameterTypes.STRING:
+                    return False
+            else:
+                if not isinstance(eval(token), str):  # pylint: disable=eval-used # nosec
+                    return False
+        return True
+
+    def _to_data_binding(self) -> str:
+        """Convert operands to data binding and concatenate them in the order of postfix expression.
+
+        :return: The data binding
+        :rtype: str
+        """
+        if not self._string_concatenation:
+            error_message = (
+                "Only string concatenation expression is supported to convert to data binding, "
+                f"current expression is '{self.expression}'."
+            )
+            raise UserErrorException(message=error_message, no_personal_data_message=error_message)
+
+        stack = []
+        for token in self._postfix:
+            if token != PipelineExpressionOperator.ADD:
+                if token in self._inputs:
+                    stack.append(self._inputs[token].value._data_binding())
+                else:
+                    stack.append(eval(token))  # pylint: disable=eval-used # nosec
+                continue
+            operand2, operand1 = stack.pop(), stack.pop()
+            stack.append(operand1 + operand2)
+        res: str = stack.pop()
+        return res
+
+    def resolve(self) -> Union[str, "BaseNode"]:
+        """Resolve expression to data binding or component, depend on the operations.
+
+        :return: The data binding string or the component
+        :rtype: Union[str, BaseNode]
+        """
+        if self._string_concatenation:
+            return self._to_data_binding()
+        return cast(Union[str, "BaseNode"], self._create_component())
+
+    @staticmethod
+    def parse_pipeline_inputs_from_data_binding(data_binding: str) -> List[str]:
+        """Parse all PipelineInputs name from data binding expression.
+
+        :param data_binding: Data binding expression
+        :type data_binding: str
+        :return: List of PipelineInput's name from given data binding expression
+        :rtype: List[str]
+        """
+        pipeline_input_names = []
+        for single_data_binding in get_all_data_binding_expressions(
+            value=data_binding,
+            binding_prefix=PipelineExpression._PIPELINE_INPUT_PREFIX,
+            is_singular=False,
+        ):
+            m = PipelineExpression._PIPELINE_INPUT_PATTERN.match(single_data_binding)
+            # `get_all_data_binding_expressions` should work as pre-filter, so no need to concern `m` is None
+            if m is not None:
+                pipeline_input_names.append(m.group(PipelineExpression._PIPELINE_INPUT_NAME_GROUP))
+        return pipeline_input_names
+
+    @staticmethod
+    def _get_operation_result_type(type1: str, operator: str, type2: str) -> str:
+        def _validate_operand_type(_type: str) -> None:
+            if _type != NONE_PARAMETER_TYPE and _type not in PipelineExpression._SUPPORTED_PIPELINE_INPUT_TYPES:
+                error_message = (
+                    f"Pipeline input type {_type!r} is not supported in expression; "
+                    f"currently only support None, "
+                    + ", ".join(PipelineExpression._SUPPORTED_PIPELINE_INPUT_TYPES)
+                    + "."
+                )
+                raise UserErrorException(message=error_message, no_personal_data_message=error_message)
+
+        _validate_operand_type(type1)
+        _validate_operand_type(type2)
+        operation = f"{type1} {operator} {type2}"
+        lookup_value = _OPERATION_RESULT_TYPE_LOOKUP.get(operation)
+        if isinstance(lookup_value, str):
+            return lookup_value  # valid operation, return result type
+        _user_exception: UserErrorException = lookup_value
+        raise _user_exception  # invalid operation, raise UserErrorException
+
+    def _get_operand_type(self, operand: str) -> str:
+        if operand in self._inputs:
+            res: str = self._inputs[operand].type
+            return res
+        primitive_type = type(eval(operand))  # pylint: disable=eval-used # nosec
+        res_type: str = IOConstants.PRIMITIVE_TYPE_2_STR.get(primitive_type, NONE_PARAMETER_TYPE)
+        return res_type
+
+    @property
+    def _component_code(self) -> str:
+        def _generate_function_code_lines() -> Tuple[List[str], str]:
+            """Return lines of code and return type.
+
+            :return: A 2-tuple of (function body, return type name)
+            :rtype: Tuple[List[str], str]
+            """
+            _inter_id, _code, _stack = 0, [], []
+            _line_recorder: Dict = {}
+            for _token in self._postfix:
+                if _token not in _SUPPORTED_OPERATORS:
+                    _type = self._get_operand_type(_token)
+                    _stack.append((_token, _type))
+                    continue
+                _operand2, _type2 = _stack.pop()
+                _operand1, _type1 = _stack.pop()
+                _current_line = f"{_operand1} {_token} {_operand2}"
+                if _current_line in _line_recorder:
+                    _inter_var, _inter_var_type = _line_recorder[_current_line]
+                else:
+                    _inter_var = f"inter_var_{_inter_id}"
+                    _inter_id += 1
+                    _inter_var_type = self._get_operation_result_type(_type1, _token, _type2)
+                    _code.append(f"{self._INDENTATION}{_inter_var} = {_current_line}")
+                    _line_recorder[_current_line] = (_inter_var, _inter_var_type)
+                _stack.append((_inter_var, _inter_var_type))
+            _return_var, _result_type = _stack.pop()
+            _code.append(f"{self._INDENTATION}return {_return_var}")
+            return _code, _result_type
+
+        def _generate_function_decorator_and_declaration_lines(_return_type: str) -> List[str]:
+            # decorator parameters
+            _display_name = f'{self._INDENTATION}display_name="Expression: {self.expression}",'
+            _decorator_parameters = "\n" + "\n".join([_display_name]) + "\n"
+            # component parameters
+            _component_parameters = []
+            for _name in sorted(self._inputs):
+                _type = self._TO_PYTHON_TYPE[self._inputs[_name].type]
+                _component_parameters.append(f"{_name}: {_type}")
+            _component_parameters_str = (
+                "\n"
+                + "\n".join(
+                    [f"{self._INDENTATION}{_component_parameter}," for _component_parameter in _component_parameters]
+                )
+                + "\n"
+            )
+            return [
+                self._IMPORT_MLDESIGNER_LINE + "\n\n",
+                self._DECORATOR_LINE.replace("@@decorator_parameters@@", _decorator_parameters),
+                self._COMPONENT_FUNC_DECLARATION_LINE.replace(
+                    "@@component_parameters@@", _component_parameters_str
+                ).replace("@@return_type@@", f'"{_return_type}"'),
+            ]
+
+        lines, result_type = _generate_function_code_lines()
+        self._result_type = result_type
+        code = _generate_function_decorator_and_declaration_lines(result_type) + lines
+        return "\n".join(code) + "\n"
+
+    def _create_component(self) -> Any:
+        def _generate_python_file(_folder: Path) -> None:
+            _folder.mkdir()
+            with open(_folder / "expression_component.py", "w", encoding=DefaultOpenEncoding.WRITE) as _f:
+                _f.write(self._component_code)
+
+        def _generate_yaml_file(_path: Path) -> None:
+            _data_folder = Path(__file__).parent / "data"
+            # update YAML content from template and dump
+            with open(_data_folder / "expression_component_template.yml", "r", encoding=DefaultOpenEncoding.READ) as _f:
+                _data = load_yaml(_f)
+            _data["display_name"] = f"Expression: {self.expression}"
+            _data["inputs"] = {}
+            _data["outputs"]["output"]["type"] = self._result_type
+            _command_inputs_items = []
+            for _name in sorted(self._inputs):
+                _type = self._inputs[_name].type
+                _data["inputs"][_name] = {"type": _type}
+                _command_inputs_items.append(_name + '="${{inputs.' + _name + '}}"')
+            _command_inputs_string = " ".join(_command_inputs_items)
+            _command_output_string = 'output="${{outputs.output}}"'
+            _command = (
+                "mldesigner execute --source expression_component.py --name expression_func"
+                " --inputs " + _command_inputs_string + " --outputs " + _command_output_string
+            )
+            _data["command"] = _data["command"].format(command_placeholder=_command)
+            dump_yaml_to_file(_path, _data)
+
+        if self._created_component is None:
+            tmp_folder = Path(tempfile.mkdtemp())
+            code_folder = tmp_folder / "src"
+            yaml_path = tmp_folder / "component_spec.yml"
+            _generate_python_file(code_folder)
+            _generate_yaml_file(yaml_path)
+
+            from azure.ai.ml import load_component
+
+            component_func = load_component(yaml_path)
+            component_kwargs = {k: v.value for k, v in self._inputs.items()}
+            self._created_component = component_func(**component_kwargs)
+            if self._created_component is not None:
+                self._created_component.environment_variables = {AZUREML_PRIVATE_FEATURES_ENV_VAR: "true"}
+        return self._created_component
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_pipeline_job_helpers.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_pipeline_job_helpers.py
new file mode 100644
index 00000000..3a7d89e7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/_pipeline_job_helpers.py
@@ -0,0 +1,182 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+import re
+from typing import Dict, List, Tuple, Type, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import InputDeliveryMode
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobInput as RestJobInput
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobOutput as RestJobOutput
+from azure.ai.ml._restclient.v2023_04_01_preview.models import Mpi, PyTorch, Ray, TensorFlow
+from azure.ai.ml.constants._component import ComponentJobConstants
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._job._input_output_helpers import (
+    INPUT_MOUNT_MAPPING_FROM_REST,
+    INPUT_MOUNT_MAPPING_TO_REST,
+    OUTPUT_MOUNT_MAPPING_FROM_REST,
+    OUTPUT_MOUNT_MAPPING_TO_REST,
+)
+from azure.ai.ml.entities._util import normalize_job_input_output_type
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+def process_sdk_component_job_io(
+    io: Dict,
+    io_binding_regex_list: List[str],
+) -> Tuple:
+    """Separates SDK ComponentJob inputs that are data bindings (i.e. string inputs prefixed with 'inputs.' or
+    'outputs.') and dataset and literal inputs/outputs.
+
+    :param io: Input or output dictionary of an SDK ComponentJob
+    :type io:  Dict[str, Union[str, float, bool, Input]]
+    :param io_binding_regex_list: A list of regexes for io bindings
+    :type io_binding_regex_list: List[str]
+    :return: A tuple of dictionaries:
+      * One mapping inputs to REST formatted ComponentJobInput/ComponentJobOutput for data binding io.
+      * The other dictionary contains any IO that is not a databinding that is yet to be turned into REST form
+    :rtype: Tuple[Dict[str, str], Dict[str, Union[str, float, bool, Input]]]
+    """
+    io_bindings: Dict = {}
+    dataset_literal_io: Dict = {}
+    legacy_io_binding_regex_list = [
+        ComponentJobConstants.LEGACY_INPUT_PATTERN,
+        ComponentJobConstants.LEGACY_OUTPUT_PATTERN,
+    ]
+    for io_name, io_value in io.items():
+        if isinstance(io_value, (Input, Output)) and isinstance(io_value.path, str):
+            mode = io_value.mode
+            path = io_value.path
+            name = io_value.name if hasattr(io_value, "name") else None
+            version = io_value.version if hasattr(io_value, "version") else None
+            if any(re.match(item, path) for item in io_binding_regex_list):
+                # Yaml syntax requires using ${{}} to enclose inputs and outputs bindings
+                # io_bindings[io_name] = io_value
+                io_bindings.update({io_name: {"value": path}})
+                # add mode to literal value for binding input
+                if mode:
+                    if isinstance(io_value, Input):
+                        io_bindings[io_name].update({"mode": INPUT_MOUNT_MAPPING_TO_REST[mode]})
+                    else:
+                        io_bindings[io_name].update({"mode": OUTPUT_MOUNT_MAPPING_TO_REST[mode]})
+                if name or version:
+                    assert isinstance(io_value, Output)
+                    if name:
+                        io_bindings[io_name].update({"name": name})
+                    if version:
+                        io_bindings[io_name].update({"version": version})
+                if isinstance(io_value, Output) and io_value.name is not None:
+                    # when the output should be registered,
+                    # we add io_value to dataset_literal_io for further to_rest_data_outputs
+                    dataset_literal_io[io_name] = io_value
+            elif any(re.match(item, path) for item in legacy_io_binding_regex_list):
+                new_format = path.replace("{{", "{{parent.")
+                msg = "{} has changed to {}, please change to use new format."
+                raise ValidationException(
+                    message=msg.format(path, new_format),
+                    no_personal_data_message=msg.format("[io_value]", "[io_value_new_format]"),
+                    target=ErrorTarget.PIPELINE,
+                    error_category=ErrorCategory.USER_ERROR,
+                )
+            else:
+                dataset_literal_io[io_name] = io_value
+        else:
+            # Collect non-input data inputs
+            dataset_literal_io[io_name] = io_value
+    return io_bindings, dataset_literal_io
+
+
+def from_dict_to_rest_io(
+    io: Dict[str, Union[str, dict]],
+    rest_object_class: Union[Type[RestJobInput], Type[RestJobOutput]],
+    io_binding_regex_list: List[str],
+) -> Tuple[Dict[str, str], Dict[str, Union[RestJobInput, RestJobOutput]]]:
+    """Translate rest JObject dictionary to rest inputs/outputs and bindings.
+
+    :param io: Input or output dictionary.
+    :type io: Dict[str, Union[str, dict]]
+    :param rest_object_class: RestJobInput or RestJobOutput
+    :type rest_object_class: Union[Type[RestJobInput], Type[RestJobOutput]]
+    :param io_binding_regex_list: A list of regexes for io bindings
+    :type io_binding_regex_list: List[str]
+    :return: Map from IO name to IO bindings and Map from IO name to IO objects.
+    :rtype: Tuple[Dict[str, str], Dict[str, Union[RestJobInput, RestJobOutput]]]
+    """
+    io_bindings: dict = {}
+    rest_io_objects = {}
+    DIRTY_MODE_MAPPING = {
+        "Mount": InputDeliveryMode.READ_ONLY_MOUNT,
+        "RoMount": InputDeliveryMode.READ_ONLY_MOUNT,
+        "RwMount": InputDeliveryMode.READ_WRITE_MOUNT,
+    }
+    for key, val in io.items():
+        if isinstance(val, dict):
+            # convert the input of camel to snake to be compatible with the Jun api
+            # todo: backend help convert node level input/output type
+            normalize_job_input_output_type(val)
+
+            # Add casting as sometimes we got value like 1(int)
+            io_value = str(val.get("value", ""))
+            io_mode = val.get("mode", None)
+            io_name = val.get("name", None)
+            io_version = val.get("version", None)
+            if any(re.match(item, io_value) for item in io_binding_regex_list):
+                io_bindings.update({key: {"path": io_value}})
+                # add mode to literal value for binding input
+                if io_mode:
+                    # deal with dirty mode data submitted before
+                    if io_mode in DIRTY_MODE_MAPPING:
+                        io_mode = DIRTY_MODE_MAPPING[io_mode]
+                        val["mode"] = io_mode
+                    if io_mode in OUTPUT_MOUNT_MAPPING_FROM_REST:
+                        io_bindings[key].update({"mode": OUTPUT_MOUNT_MAPPING_FROM_REST[io_mode]})
+                    else:
+                        io_bindings[key].update({"mode": INPUT_MOUNT_MAPPING_FROM_REST[io_mode]})
+                # add name and version for binding input
+                if io_name or io_version:
+                    assert rest_object_class.__name__ == "JobOutput"
+                    # current code only support dump name and version for JobOutput
+                    # this assert can be deleted if we need to dump name/version for JobInput
+                    if io_name:
+                        io_bindings[key].update({"name": io_name})
+                    if io_version:
+                        io_bindings[key].update({"version": io_version})
+                if not io_mode and not io_name and not io_version:
+                    io_bindings[key] = io_value
+            else:
+                if rest_object_class.__name__ == "JobOutput":
+                    # current code only support dump name and version for JobOutput
+                    # this condition can be deleted if we need to dump name/version for JobInput
+                    if "name" in val.keys():
+                        val["asset_name"] = val.pop("name")
+                    if "version" in val.keys():
+                        val["asset_version"] = val.pop("version")
+                rest_obj = rest_object_class.from_dict(val)
+                rest_io_objects[key] = rest_obj
+        else:
+            msg = "Got unsupported type of input/output: {}:" + f"{type(val)}"
+            raise ValidationException(
+                message=msg.format(val),
+                no_personal_data_message=msg.format("[val]"),
+                target=ErrorTarget.PIPELINE,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+    return io_bindings, rest_io_objects
+
+
+def from_dict_to_rest_distribution(distribution_dict: Dict) -> Union[PyTorch, Mpi, TensorFlow, Ray]:
+    target_type = distribution_dict["distribution_type"].lower()
+    if target_type == "pytorch":
+        return PyTorch(**distribution_dict)
+    if target_type == "mpi":
+        return Mpi(**distribution_dict)
+    if target_type == "tensorflow":
+        return TensorFlow(**distribution_dict)
+    if target_type == "ray":
+        return Ray(**distribution_dict)
+    msg = "Distribution type must be pytorch, mpi, tensorflow or ray: {}".format(target_type)
+    raise ValidationException(
+        message=msg,
+        no_personal_data_message=msg,
+        target=ErrorTarget.PIPELINE,
+        error_category=ErrorCategory.USER_ERROR,
+    )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/data/expression_component_template.yml b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/data/expression_component_template.yml
new file mode 100644
index 00000000..10d391aa
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/data/expression_component_template.yml
@@ -0,0 +1,16 @@
+$schema: https://azuremlschemas.azureedge.net/latest/commandComponent.schema.json
+type: command
+
+name: expression_component
+version: 1
+
+outputs:
+  output:
+    is_control: true
+
+code: ./src
+
+environment: azureml://registries/azureml/environments/mldesigner/labels/latest
+
+command: >-
+  {command_placeholder}
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/pipeline_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/pipeline_job.py
new file mode 100644
index 00000000..7ddbbc46
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/pipeline_job.py
@@ -0,0 +1,711 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+import itertools
+import logging
+import typing
+from functools import partial
+from pathlib import Path
+from typing import Any, Dict, Generator, List, Optional, Union, cast
+
+from typing_extensions import Literal
+
+from azure.ai.ml._restclient.v2024_01_01_preview.models import JobBase
+from azure.ai.ml._restclient.v2024_01_01_preview.models import PipelineJob as RestPipelineJob
+from azure.ai.ml._schema import PathAwareSchema
+from azure.ai.ml._schema.pipeline.pipeline_job import PipelineJobSchema
+from azure.ai.ml._utils._arm_id_utils import get_resource_name_from_arm_id_safe
+from azure.ai.ml._utils.utils import (
+    camel_to_snake,
+    is_data_binding_expression,
+    is_private_preview_enabled,
+    transform_dict_keys,
+)
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.constants._common import AZUREML_PRIVATE_FEATURES_ENV_VAR, BASE_PATH_CONTEXT_KEY
+from azure.ai.ml.constants._component import ComponentSource
+from azure.ai.ml.constants._job.pipeline import ValidationErrorCode
+from azure.ai.ml.entities._builders import BaseNode
+from azure.ai.ml.entities._builders.condition_node import ConditionNode
+from azure.ai.ml.entities._builders.control_flow_node import LoopNode
+from azure.ai.ml.entities._builders.import_node import Import
+from azure.ai.ml.entities._builders.parallel import Parallel
+from azure.ai.ml.entities._builders.pipeline import Pipeline
+from azure.ai.ml.entities._component.component import Component
+from azure.ai.ml.entities._component.pipeline_component import PipelineComponent
+
+# from azure.ai.ml.entities._job.identity import AmlToken, Identity, ManagedIdentity, UserIdentity
+from azure.ai.ml.entities._credentials import (
+    AmlTokenConfiguration,
+    ManagedIdentityConfiguration,
+    UserIdentityConfiguration,
+    _BaseJobIdentityConfiguration,
+)
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._inputs_outputs.group_input import GroupInput
+from azure.ai.ml.entities._job._input_output_helpers import (
+    from_rest_data_outputs,
+    from_rest_inputs_to_dataset_literal,
+    to_rest_data_outputs,
+    to_rest_dataset_literal_inputs,
+)
+from azure.ai.ml.entities._job.import_job import ImportJob
+from azure.ai.ml.entities._job.job import Job
+from azure.ai.ml.entities._job.job_service import JobServiceBase
+from azure.ai.ml.entities._job.pipeline._io import PipelineInput, PipelineJobIOMixin
+from azure.ai.ml.entities._job.pipeline.pipeline_job_settings import PipelineJobSettings
+from azure.ai.ml.entities._mixins import YamlTranslatableMixin
+from azure.ai.ml.entities._system_data import SystemData
+from azure.ai.ml.entities._validation import MutableValidationResult, PathAwareSchemaValidatableMixin
+from azure.ai.ml.exceptions import ErrorTarget, UserErrorException, ValidationException
+
+module_logger = logging.getLogger(__name__)
+
+
+class PipelineJob(Job, YamlTranslatableMixin, PipelineJobIOMixin, PathAwareSchemaValidatableMixin):
+    """Pipeline job.
+
+    You should not instantiate this class directly. Instead, you should
+    use the `@pipeline` decorator to create a `PipelineJob`.
+
+    :param component: Pipeline component version. The field is mutually exclusive with 'jobs'.
+    :type component: Union[str, ~azure.ai.ml.entities._component.pipeline_component.PipelineComponent]
+    :param inputs: Inputs to the pipeline job.
+    :type inputs: dict[str, Union[~azure.ai.ml.entities.Input, str, bool, int, float]]
+    :param outputs: Outputs of the pipeline job.
+    :type outputs: dict[str, ~azure.ai.ml.entities.Output]
+    :param name: Name of the PipelineJob. Defaults to None.
+    :type name: str
+    :param description: Description of the pipeline job. Defaults to None
+    :type description: str
+    :param display_name: Display name of the pipeline job. Defaults to None
+    :type display_name: str
+    :param experiment_name: Name of the experiment the job will be created under.
+        If None is provided, the experiment will be set to the current directory. Defaults to None
+    :type experiment_name: str
+    :param jobs: Pipeline component node name to component object. Defaults to None
+    :type jobs: dict[str, ~azure.ai.ml.entities._builders.BaseNode]
+    :param settings: Setting of the pipeline job. Defaults to None
+    :type settings: ~azure.ai.ml.entities.PipelineJobSettings
+    :param identity: Identity that the training job will use while running on compute. Defaults to None
+    :type identity: Union[
+        ~azure.ai.ml.entities._credentials.ManagedIdentityConfiguration,
+        ~azure.ai.ml.entities._credentials.AmlTokenConfiguration,
+        ~azure.ai.ml.entities._credentials.UserIdentityConfiguration
+
+    ]
+    :param compute: Compute target name of the built pipeline. Defaults to None
+    :type compute: str
+    :param tags: Tag dictionary. Tags can be added, removed, and updated. Defaults to None
+    :type tags: dict[str, str]
+    :param kwargs: A dictionary of additional configuration parameters. Defaults to None
+    :type kwargs: dict
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_pipeline_job_configurations.py
+            :start-after: [START configure_pipeline_job_and_settings]
+            :end-before: [END configure_pipeline_job_and_settings]
+            :language: python
+            :dedent: 8
+            :caption: Shows how to create a pipeline using this class.
+    """
+
+    def __init__(
+        self,
+        *,
+        component: Optional[Union[str, PipelineComponent, Component]] = None,
+        inputs: Optional[Dict[str, Union[Input, str, bool, int, float]]] = None,
+        outputs: Optional[Dict[str, Output]] = None,
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        display_name: Optional[str] = None,
+        experiment_name: Optional[str] = None,
+        jobs: Optional[Dict[str, BaseNode]] = None,
+        settings: Optional[PipelineJobSettings] = None,
+        identity: Optional[
+            Union[ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]
+        ] = None,
+        compute: Optional[str] = None,
+        tags: Optional[Dict[str, str]] = None,
+        **kwargs: Any,
+    ) -> None:
+        # initialize io
+        inputs, outputs = inputs or {}, outputs or {}
+        if isinstance(component, PipelineComponent) and component._source in [
+            ComponentSource.DSL,
+            ComponentSource.YAML_COMPONENT,
+        ]:
+            self._inputs = self._build_inputs_dict(inputs, input_definition_dict=component.inputs)
+            # for pipeline component created pipeline jobs,
+            # it's output should have same value with the component outputs,
+            # then override it with given outputs (filter out None value)
+            pipeline_outputs = {k: v for k, v in (outputs or {}).items() if v}
+            self._outputs = self._build_pipeline_outputs_dict({**component.outputs, **pipeline_outputs})
+        else:
+            # Build inputs/outputs dict without meta when definition not available
+            self._inputs = self._build_inputs_dict(inputs)
+            # for node created pipeline jobs,
+            # it's output should have same value with the given outputs
+            self._outputs = self._build_pipeline_outputs_dict(outputs=outputs)
+        source = kwargs.pop("_source", ComponentSource.CLASS)
+        if component is None:
+            component = PipelineComponent(
+                jobs=jobs,
+                description=description,
+                display_name=display_name,
+                base_path=kwargs.get(BASE_PATH_CONTEXT_KEY),
+                _source=source,
+            )
+
+        # If component is Pipeline component, jobs will be component.jobs
+        self._jobs = (jobs or {}) if isinstance(component, str) else {}
+
+        self.component: Union[PipelineComponent, str] = cast(Union[PipelineComponent, str], component)
+        if "type" not in kwargs:
+            kwargs["type"] = JobType.PIPELINE
+        if isinstance(component, PipelineComponent):
+            description = component.description if description is None else description
+            display_name = component.display_name if display_name is None else display_name
+        super(PipelineJob, self).__init__(
+            name=name,
+            description=description,
+            tags=tags,
+            display_name=display_name,
+            experiment_name=experiment_name,
+            compute=compute,
+            **kwargs,
+        )
+
+        self._remove_pipeline_input()
+        self.compute = compute
+        self._settings: Any = None
+        self.settings = settings
+        self.identity = identity
+        # TODO: remove default code & environment?
+        self._default_code = None
+        self._default_environment = None
+
+    @property
+    def inputs(self) -> Dict:
+        """Inputs of the pipeline job.
+
+        :return: Inputs of the pipeline job.
+        :rtype: dict[str, Union[~azure.ai.ml.entities.Input, str, bool, int, float]]
+        """
+        return self._inputs
+
+    @property
+    def outputs(self) -> Dict[str, Union[str, Output]]:
+        """Outputs of the pipeline job.
+
+        :return: Outputs of the pipeline job.
+        :rtype: dict[str, Union[str, ~azure.ai.ml.entities.Output]]
+        """
+        return self._outputs
+
+    @property
+    def jobs(self) -> Dict:
+        """Return jobs of pipeline job.
+
+        :return: Jobs of pipeline job.
+        :rtype: dict
+        """
+        res: dict = self.component.jobs if isinstance(self.component, PipelineComponent) else self._jobs
+        return res
+
+    @property
+    def settings(self) -> Optional[PipelineJobSettings]:
+        """Settings of the pipeline job.
+
+        :return: Settings of the pipeline job.
+        :rtype: ~azure.ai.ml.entities.PipelineJobSettings
+        """
+        if self._settings is None:
+            self._settings = PipelineJobSettings()
+        res: Optional[PipelineJobSettings] = self._settings
+        return res
+
+    @settings.setter
+    def settings(self, value: Union[Dict, PipelineJobSettings]) -> None:
+        """Set the pipeline job settings.
+
+        :param value: The pipeline job settings.
+        :type value: Union[dict, ~azure.ai.ml.entities.PipelineJobSettings]
+        """
+        if value is not None:
+            if isinstance(value, PipelineJobSettings):
+                # since PipelineJobSettings inherit _AttrDict, we need add this branch to distinguish with dict
+                pass
+            elif isinstance(value, dict):
+                value = PipelineJobSettings(**value)
+            else:
+                raise TypeError("settings must be PipelineJobSettings or dict but got {}".format(type(value)))
+        self._settings = value
+
+    @classmethod
+    def _create_validation_error(cls, message: str, no_personal_data_message: str) -> ValidationException:
+        return ValidationException(
+            message=message,
+            no_personal_data_message=no_personal_data_message,
+            target=ErrorTarget.PIPELINE,
+        )
+
+    @classmethod
+    def _create_schema_for_validation(cls, context: Any) -> PathAwareSchema:
+        # import this to ensure that nodes are registered before schema is created.
+
+        return PipelineJobSchema(context=context)
+
+    @classmethod
+    def _get_skip_fields_in_schema_validation(cls) -> typing.List[str]:
+        # jobs validations are done in _customized_validate()
+        return ["component", "jobs"]
+
+    @property
+    def _skip_required_compute_missing_validation(self) -> Literal[True]:
+        return True
+
+    def _validate_compute_is_set(self) -> MutableValidationResult:
+        validation_result = self._create_empty_validation_result()
+        if self.compute is not None:
+            return validation_result
+        if self.settings is not None and self.settings.default_compute is not None:
+            return validation_result
+
+        if not isinstance(self.component, str):
+            validation_result.merge_with(self.component._validate_compute_is_set())
+        return validation_result
+
+    def _customized_validate(self) -> MutableValidationResult:
+        """Validate that all provided inputs and parameters are valid for current pipeline and components in it.
+
+        :return: The validation result
+        :rtype: MutableValidationResult
+        """
+        validation_result = super(PipelineJob, self)._customized_validate()
+
+        if isinstance(self.component, PipelineComponent):
+            # Merge with pipeline component validate result for structure validation.
+            # Skip top level parameter missing type error
+            validation_result.merge_with(
+                self.component._customized_validate(),
+                condition_skip=lambda x: x.error_code == ValidationErrorCode.PARAMETER_TYPE_UNKNOWN
+                and x.yaml_path.startswith("inputs"),
+            )
+            # Validate compute
+            validation_result.merge_with(self._validate_compute_is_set())
+        # Validate Input
+        validation_result.merge_with(self._validate_input())
+        # Validate initialization & finalization jobs
+        validation_result.merge_with(self._validate_init_finalize_job())
+
+        return validation_result
+
+    def _validate_input(self) -> MutableValidationResult:
+        validation_result = self._create_empty_validation_result()
+        if not isinstance(self.component, str):
+            # TODO(1979547): refine this logic: not all nodes have `_get_input_binding_dict` method
+            used_pipeline_inputs = set(
+                itertools.chain(
+                    *[
+                        self.component._get_input_binding_dict(node if not isinstance(node, LoopNode) else node.body)[0]
+                        for node in self.jobs.values()
+                        if not isinstance(node, ConditionNode)
+                        # condition node has no inputs
+                    ]
+                )
+            )
+        # validate inputs
+        if not isinstance(self.component, Component):
+            return validation_result
+        for key, meta in self.component.inputs.items():
+            if key not in used_pipeline_inputs:  # pylint: disable=possibly-used-before-assignment
+                # Only validate inputs certainly used.
+                continue
+            # raise error when required input with no default value not set
+            if (
+                self.inputs.get(key, None) is None  # input not provided
+                and meta.optional is not True  # and it's required
+                and meta.default is None  # and it does not have default
+            ):
+                name = self.name or self.display_name
+                name = f"{name!r} " if name else ""
+                validation_result.append_error(
+                    yaml_path=f"inputs.{key}",
+                    message=f"Required input {key!r} for pipeline {name}not provided.",
+                )
+        return validation_result
+
+    def _validate_init_finalize_job(self) -> MutableValidationResult:  # pylint: disable=too-many-statements
+        from azure.ai.ml.entities._job.pipeline._io import InputOutputBase, _GroupAttrDict
+
+        validation_result = self._create_empty_validation_result()
+        # subgraph (PipelineComponent) should not have on_init/on_finalize set
+        for job_name, job in self.jobs.items():
+            if job.type != "pipeline":
+                continue
+            if job.settings.on_init:
+                validation_result.append_error(
+                    yaml_path=f"jobs.{job_name}.settings.on_init",
+                    message="On_init is not supported for pipeline component.",
+                )
+            if job.settings.on_finalize:
+                validation_result.append_error(
+                    yaml_path=f"jobs.{job_name}.settings.on_finalize",
+                    message="On_finalize is not supported for pipeline component.",
+                )
+
+        on_init = None
+        on_finalize = None
+
+        if self.settings is not None:
+            # quick return if neither on_init nor on_finalize is set
+            if self.settings.on_init is None and self.settings.on_finalize is None:
+                return validation_result
+
+            on_init, on_finalize = self.settings.on_init, self.settings.on_finalize
+
+        append_on_init_error = partial(validation_result.append_error, "settings.on_init")
+        append_on_finalize_error = partial(validation_result.append_error, "settings.on_finalize")
+        # on_init and on_finalize cannot be same
+        if on_init == on_finalize:
+            append_on_init_error(f"Invalid on_init job {on_init}, it should be different from on_finalize.")
+            append_on_finalize_error(f"Invalid on_finalize job {on_finalize}, it should be different from on_init.")
+        # pipeline should have at least one normal node
+        if len(set(self.jobs.keys()) - {on_init, on_finalize}) == 0:
+            validation_result.append_error(yaml_path="jobs", message="No other job except for on_init/on_finalize job.")
+
+        def _is_control_flow_node(_validate_job_name: str) -> bool:
+            from azure.ai.ml.entities._builders.control_flow_node import ControlFlowNode
+
+            _validate_job = self.jobs[_validate_job_name]
+            return issubclass(type(_validate_job), ControlFlowNode)
+
+        def _is_isolated_job(_validate_job_name: str) -> bool:
+            def _try_get_data_bindings(
+                _name: str, _input_output_data: Union["_GroupAttrDict", "InputOutputBase"]
+            ) -> Optional[List]:
+                """Try to get data bindings from input/output data, return None if not found.
+                :param _name: The name to use when flattening GroupAttrDict
+                :type _name: str
+                :param _input_output_data: The input/output data
+                :type _input_output_data: Union[_GroupAttrDict, str, InputOutputBase]
+                :return: A list of data bindings, or None if not found
+                :rtype: Optional[List[str]]
+                """
+                # handle group input
+                if GroupInput._is_group_attr_dict(_input_output_data):
+                    _new_input_output_data: _GroupAttrDict = cast(_GroupAttrDict, _input_output_data)
+                    # flatten to avoid nested cases
+                    flattened_values: List[Input] = list(_new_input_output_data.flatten(_name).values())
+                    # handle invalid empty group
+                    if len(flattened_values) == 0:
+                        return None
+                    return [_value.path for _value in flattened_values]
+                _input_output_data = _input_output_data._data
+                if isinstance(_input_output_data, str):
+                    return [_input_output_data]
+                if not hasattr(_input_output_data, "_data_binding"):
+                    return None
+                return [_input_output_data._data_binding()]
+
+            _validate_job = self.jobs[_validate_job_name]
+            # no input to validate job
+            for _input_name in _validate_job.inputs:
+                _data_bindings = _try_get_data_bindings(_input_name, _validate_job.inputs[_input_name])
+                if _data_bindings is None:
+                    continue
+                for _data_binding in _data_bindings:
+                    if is_data_binding_expression(_data_binding, ["parent", "jobs"]):
+                        return False
+            # no output from validate job - iterate other jobs input(s) to validate
+            for _job_name, _job in self.jobs.items():
+                # exclude control flow node as it does not have inputs
+                if _is_control_flow_node(_job_name):
+                    continue
+                for _input_name in _job.inputs:
+                    _data_bindings = _try_get_data_bindings(_input_name, _job.inputs[_input_name])
+                    if _data_bindings is None:
+                        continue
+                    for _data_binding in _data_bindings:
+                        if is_data_binding_expression(_data_binding, ["parent", "jobs", _validate_job_name]):
+                            return False
+            return True
+
+        # validate on_init
+        if on_init is not None:
+            if on_init not in self.jobs:
+                append_on_init_error(f"On_init job name {on_init} not exists in jobs.")
+            else:
+                if _is_control_flow_node(on_init):
+                    append_on_init_error("On_init job should not be a control flow node.")
+                elif not _is_isolated_job(on_init):
+                    append_on_init_error("On_init job should not have connection to other execution node.")
+        # validate on_finalize
+        if on_finalize is not None:
+            if on_finalize not in self.jobs:
+                append_on_finalize_error(f"On_finalize job name {on_finalize} not exists in jobs.")
+            else:
+                if _is_control_flow_node(on_finalize):
+                    append_on_finalize_error("On_finalize job should not be a control flow node.")
+                elif not _is_isolated_job(on_finalize):
+                    append_on_finalize_error("On_finalize job should not have connection to other execution node.")
+        return validation_result
+
+    def _remove_pipeline_input(self) -> None:
+        """Remove None pipeline input.If not remove, it will pass "None" to backend."""
+        redundant_pipeline_inputs = []
+        for pipeline_input_name, pipeline_input in self._inputs.items():
+            if isinstance(pipeline_input, PipelineInput) and pipeline_input._data is None:
+                redundant_pipeline_inputs.append(pipeline_input_name)
+        for redundant_pipeline_input in redundant_pipeline_inputs:
+            self._inputs.pop(redundant_pipeline_input)
+
+    def _check_private_preview_features(self) -> None:
+        """Checks is private preview features included in pipeline.
+
+        If private preview environment not set, raise exception.
+        """
+        if not is_private_preview_enabled():
+            error_msg = (
+                "{} is a private preview feature, "
+                f"please set environment variable {AZUREML_PRIVATE_FEATURES_ENV_VAR} to true to use it."
+            )
+            # check has not supported nodes
+            for _, node in self.jobs.items():
+                # TODO: Remove in PuP
+                if isinstance(node, (ImportJob, Import)):
+                    msg = error_msg.format("Import job in pipeline")
+                    raise UserErrorException(message=msg, no_personal_data_message=msg)
+
+    def _to_node(self, context: Optional[Dict] = None, **kwargs: Any) -> "Pipeline":
+        """Translate a command job to a pipeline node when load schema.
+
+        (Write a pipeline job as node in yaml is not supported presently.)
+
+        :param context: Context of command job YAML file.
+        :type context: dict
+        :return: Translated command component.
+        :rtype: Pipeline
+        """
+        component = self._to_component(context, **kwargs)
+
+        return Pipeline(
+            component=component,
+            compute=self.compute,
+            # Need to supply the inputs with double curly.
+            inputs=self.inputs,
+            outputs=self.outputs,
+            description=self.description,
+            tags=self.tags,
+            display_name=self.display_name,
+            properties=self.properties,
+        )
+
+    def _to_rest_object(self) -> JobBase:
+        """Build current parameterized pipeline instance to a pipeline job object before submission.
+
+        :return: Rest pipeline job.
+        :rtype: JobBase
+        """
+        # Check if there are private preview features in it
+        self._check_private_preview_features()
+
+        # Build the inputs to dict. Handle both value & binding assignment.
+        # Example: {
+        #   "input_data": {"data": {"path": "path/to/input/data"},  "mode"="Mount"},
+        #   "input_value": 10,
+        #   "learning_rate": "${{jobs.step1.inputs.learning_rate}}"
+        # }
+        built_inputs = self._build_inputs()
+
+        # Build the outputs to dict
+        # example: {"eval_output": "${{jobs.eval.outputs.eval_output}}"}
+        built_outputs = self._build_outputs()
+
+        if self.settings is not None:
+            settings_dict = self.settings._to_dict()
+
+        if isinstance(self.component, PipelineComponent):
+            source = self.component._source
+            # Build the jobs to dict
+            rest_component_jobs = self.component._build_rest_component_jobs()
+        else:
+            source = ComponentSource.REMOTE_WORKSPACE_JOB
+            rest_component_jobs = {}
+        # add _source on pipeline job.settings
+        if "_source" not in settings_dict:  # pylint: disable=possibly-used-before-assignment
+            settings_dict.update({"_source": source})
+
+        # TODO: Revisit this logic when multiple types of component jobs are supported
+        rest_compute = self.compute
+        # This will be resolved in job_operations _resolve_arm_id_or_upload_dependencies.
+        component_id = self.component if isinstance(self.component, str) else self.component.id
+
+        # TODO remove it in the future.
+        # MFE not support pass None or empty input value. Remove the empty inputs in pipeline job.
+        built_inputs = {k: v for k, v in built_inputs.items() if v is not None and v != ""}
+
+        pipeline_job = RestPipelineJob(
+            compute_id=rest_compute,
+            component_id=component_id,
+            display_name=self.display_name,
+            tags=self.tags,
+            description=self.description,
+            properties=self.properties,
+            experiment_name=self.experiment_name,
+            jobs=rest_component_jobs,
+            inputs=to_rest_dataset_literal_inputs(built_inputs, job_type=self.type),
+            outputs=to_rest_data_outputs(built_outputs),
+            settings=settings_dict,
+            services={k: v._to_rest_object() for k, v in self.services.items()} if self.services else None,
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+        )
+
+        rest_job = JobBase(properties=pipeline_job)
+        rest_job.name = self.name
+        return rest_job
+
+    @classmethod
+    def _load_from_rest(cls, obj: JobBase) -> "PipelineJob":
+        """Build a pipeline instance from rest pipeline object.
+
+        :param obj: The REST Pipeline Object
+        :type obj: JobBase
+        :return: pipeline job.
+        :rtype: PipelineJob
+        """
+        properties: RestPipelineJob = obj.properties
+        # Workaround for BatchEndpoint as these fields are not filled in
+        # Unpack the inputs
+        from_rest_inputs = from_rest_inputs_to_dataset_literal(properties.inputs) or {}
+        from_rest_outputs = from_rest_data_outputs(properties.outputs) or {}
+        # Unpack the component jobs
+        sub_nodes = PipelineComponent._resolve_sub_nodes(properties.jobs) if properties.jobs else {}
+        # backend may still store Camel settings, eg: DefaultDatastore, translate them to snake when load back
+        settings_dict = transform_dict_keys(properties.settings, camel_to_snake) if properties.settings else None
+        settings_sdk = PipelineJobSettings(**settings_dict) if settings_dict else PipelineJobSettings()
+        # Create component or use component id
+        if getattr(properties, "component_id", None):
+            component = properties.component_id
+        else:
+            component = PipelineComponent._load_from_rest_pipeline_job(
+                {
+                    "inputs": from_rest_inputs,
+                    "outputs": from_rest_outputs,
+                    "display_name": properties.display_name,
+                    "description": properties.description,
+                    "jobs": sub_nodes,
+                }
+            )
+
+        job = PipelineJob(
+            component=component,
+            inputs=from_rest_inputs,
+            outputs=from_rest_outputs,
+            name=obj.name,
+            id=obj.id,
+            jobs=sub_nodes,
+            display_name=properties.display_name,
+            tags=properties.tags,
+            properties=properties.properties,
+            experiment_name=properties.experiment_name,
+            status=properties.status,
+            creation_context=SystemData._from_rest_object(obj.system_data) if obj.system_data else None,
+            services=JobServiceBase._from_rest_job_services(properties.services) if properties.services else None,
+            compute=get_resource_name_from_arm_id_safe(properties.compute_id),
+            settings=settings_sdk,
+            identity=(
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+        )
+
+        return job
+
+    def _to_dict(self) -> Dict:
+        res: dict = self._dump_for_validation()
+        return res
+
+    @classmethod
+    def _component_items_from_path(cls, data: Dict) -> Generator:
+        if "jobs" in data:
+            for node_name, job_instance in data["jobs"].items():
+                potential_component_path = job_instance["component"] if "component" in job_instance else None
+                if isinstance(potential_component_path, str) and potential_component_path.startswith("file:"):
+                    yield node_name, potential_component_path
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "PipelineJob":
+        path_first_occurrence: dict = {}
+        component_first_occurrence = {}
+        for node_name, component_path in cls._component_items_from_path(data):
+            if component_path in path_first_occurrence:
+                component_first_occurrence[node_name] = path_first_occurrence[component_path]
+                # set components to be replaced here may break the validation logic
+            else:
+                path_first_occurrence[component_path] = node_name
+
+        # use this instead of azure.ai.ml.entities._util.load_from_dict to avoid parsing
+        loaded_schema = cls._create_schema_for_validation(context=context).load(data, **kwargs)
+
+        # replace repeat component with first occurrence to reduce arm id resolution
+        # current load yaml file logic is in azure.ai.ml._schema.core.schema.YamlFileSchema.load_from_file
+        # is it possible to load the same yaml file only once in 1 pipeline loading?
+        for node_name, first_occurrence in component_first_occurrence.items():
+            job = loaded_schema["jobs"][node_name]
+            job._component = loaded_schema["jobs"][first_occurrence].component
+            # For Parallel job, should also align task attribute which is usually from component.task
+            if isinstance(job, Parallel):
+                job.task = job._component.task
+                # parallel.task.code is based on parallel._component.base_path, so need to update it
+                job._base_path = job._component.base_path
+        return PipelineJob(
+            base_path=context[BASE_PATH_CONTEXT_KEY],
+            _source=ComponentSource.YAML_JOB,
+            **loaded_schema,
+        )
+
+    def __str__(self) -> str:
+        try:
+            res_to_yaml: str = self._to_yaml()
+            return res_to_yaml
+        except BaseException:  # pylint: disable=W0718
+            res: str = super(PipelineJob, self).__str__()
+            return res
+
+    def _get_telemetry_values(self) -> Dict:
+        telemetry_values: dict = super()._get_telemetry_values()
+        if isinstance(self.component, PipelineComponent):
+            telemetry_values.update(self.component._get_telemetry_values())
+        else:
+            telemetry_values.update({"source": ComponentSource.REMOTE_WORKSPACE_JOB})
+        telemetry_values.pop("is_anonymous")
+        return telemetry_values
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "PipelineComponent":
+        """Translate a pipeline job to pipeline component.
+
+        :param context: Context of pipeline job YAML file.
+        :type context: dict
+        :return: Translated pipeline component.
+        :rtype: PipelineComponent
+        """
+        ignored_keys = PipelineComponent._check_ignored_keys(self)
+        if ignored_keys:
+            name = self.name or self.display_name
+            name = f"{name!r} " if name else ""
+            module_logger.warning("%s ignored when translating PipelineJob %sto PipelineComponent.", ignored_keys, name)
+        pipeline_job_dict = kwargs.get("pipeline_job_dict", {})
+        context = context or {BASE_PATH_CONTEXT_KEY: Path("./")}
+
+        # Create anonymous pipeline component with default version as 1
+        return PipelineComponent(
+            base_path=context[BASE_PATH_CONTEXT_KEY],
+            display_name=self.display_name,
+            inputs=self._to_inputs(inputs=self.inputs, pipeline_job_dict=pipeline_job_dict),
+            outputs=self._to_outputs(outputs=self.outputs, pipeline_job_dict=pipeline_job_dict),
+            jobs=self.jobs,
+        )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/pipeline_job_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/pipeline_job_settings.py
new file mode 100644
index 00000000..0fe41e2e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/pipeline/pipeline_job_settings.py
@@ -0,0 +1,75 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Any, Dict, Generator, Optional
+
+from azure.ai.ml.entities._job.pipeline._attr_dict import _AttrDict
+
+
+class PipelineJobSettings(_AttrDict):
+    """Settings of PipelineJob.
+
+    :param default_datastore: The default datastore of the pipeline.
+    :type default_datastore: str
+    :param default_compute: The default compute target of the pipeline.
+    :type default_compute: str
+    :param continue_on_step_failure: Flag indicating whether to continue pipeline execution if a step fails.
+    :type continue_on_step_failure: bool
+    :param force_rerun: Flag indicating whether to force rerun pipeline execution.
+    :type force_rerun: bool
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_pipeline_job_configurations.py
+            :start-after: [START configure_pipeline_job_and_settings]
+            :end-before: [END configure_pipeline_job_and_settings]
+            :language: python
+            :dedent: 8
+            :caption: Shows how to set pipeline properties using this class.
+    """
+
+    def __init__(
+        self,
+        default_datastore: Optional[str] = None,
+        default_compute: Optional[str] = None,
+        continue_on_step_failure: Optional[bool] = None,
+        force_rerun: Optional[bool] = None,
+        **kwargs: Any
+    ) -> None:
+        self._init = True
+        super().__init__()
+        self.default_compute: Any = default_compute
+        self.default_datastore: Any = default_datastore
+        self.continue_on_step_failure = continue_on_step_failure
+        self.force_rerun = force_rerun
+        self.on_init = kwargs.get("on_init", None)
+        self.on_finalize = kwargs.get("on_finalize", None)
+        for k, v in kwargs.items():
+            setattr(self, k, v)
+        self._init = False
+
+    def _get_valid_keys(self) -> Generator[str, Any, None]:
+        for k, v in self.__dict__.items():
+            if v is None:
+                continue
+            # skip private attributes inherited from _AttrDict
+            if k in ["_logger", "_allowed_keys", "_init", "_key_restriction"]:
+                continue
+            yield k
+
+    def _to_dict(self) -> Dict:
+        result = {}
+        for k in self._get_valid_keys():
+            result[k] = self.__dict__[k]
+        result.update(self._get_attrs())
+        return result
+
+    def _initializing(self) -> bool:
+        return self._init
+
+    def __bool__(self) -> bool:
+        for _ in self._get_valid_keys():
+            return True
+        # _attr_dict will return False if no extra attributes are set
+        return self.__len__() > 0
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/queue_settings.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/queue_settings.py
new file mode 100644
index 00000000..5b51fb6e
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/queue_settings.py
@@ -0,0 +1,87 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import logging
+from typing import Any, Dict, Optional, Union
+
+from ..._restclient.v2023_04_01_preview.models import QueueSettings as RestQueueSettings
+from ..._utils._experimental import experimental
+from ..._utils.utils import is_data_binding_expression
+from ...constants._job.job import JobPriorityValues, JobTierNames
+from ...entities._mixins import DictMixin, RestTranslatableMixin
+from ...exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException
+
+module_logger = logging.getLogger(__name__)
+
+
+@experimental
+class QueueSettings(RestTranslatableMixin, DictMixin):
+    """Queue settings for a pipeline job.
+
+    :ivar job_tier: Enum to determine the job tier. Possible values include: "Spot", "Basic",
+        "Standard", "Premium", "Null".
+    :vartype job_tier: str or ~azure.mgmt.machinelearningservices.models.JobTier
+    :ivar priority: Controls the priority of the job on a compute.
+    :vartype priority: str
+    :keyword job_tier: The job tier. Accepted values are "Spot", "Basic", "Standard", and "Premium".
+    :paramtype job_tier: Optional[Literal]]
+    :keyword priority: The priority of the job on a compute. Accepted values are "low", "medium", and "high".
+        Defaults to "medium".
+    :paramtype priority: Optional[Literal]
+    :keyword kwargs: Additional properties for QueueSettings.
+    :paramtype kwargs: Optional[dict]
+    """
+
+    def __init__(
+        self,  # pylint: disable=unused-argument
+        *,
+        job_tier: Optional[str] = None,
+        priority: Optional[str] = None,
+        **kwargs: Any,
+    ) -> None:
+        self.job_tier = job_tier
+        self.priority = priority
+
+    def _to_rest_object(self) -> RestQueueSettings:
+        self._validate()
+        job_tier = JobTierNames.ENTITY_TO_REST.get(self.job_tier.lower(), None) if self.job_tier else None
+        priority = JobPriorityValues.ENTITY_TO_REST.get(self.priority.lower(), None) if self.priority else None
+        return RestQueueSettings(job_tier=job_tier, priority=priority)
+
+    @classmethod
+    def _from_rest_object(cls, obj: Union[Dict[str, Any], RestQueueSettings, None]) -> Optional["QueueSettings"]:
+        if obj is None:
+            return None
+        if isinstance(obj, dict):
+            queue_settings = RestQueueSettings.from_dict(obj)
+            return cls._from_rest_object(queue_settings)
+        job_tier = JobTierNames.REST_TO_ENTITY.get(obj.job_tier, None) if obj.job_tier else None
+        priority = JobPriorityValues.REST_TO_ENTITY.get(obj.priority, None) if hasattr(obj, "priority") else None
+        return cls(job_tier=job_tier, priority=priority)
+
+    def _validate(self) -> None:
+        for key, enum_class in [("job_tier", JobTierNames), ("priority", JobPriorityValues)]:
+            value = getattr(self, key)
+            if is_data_binding_expression(value):
+                msg = (
+                    f"do not support data binding expression on {key} as it involves value mapping "
+                    f"when transformed to rest object, but received '{value}'."
+                )
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.JOB,
+                    error_category=ErrorCategory.USER_ERROR,
+                    error_type=ValidationErrorType.INVALID_VALUE,
+                )
+            valid_keys = list(enum_class.ENTITY_TO_REST.keys())  # type: ignore[attr-defined]
+            if value and value.lower() not in valid_keys:
+                msg = f"{key} should be one of {valid_keys}, but received '{value}'."
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.JOB,
+                    error_category=ErrorCategory.USER_ERROR,
+                    error_type=ValidationErrorType.INVALID_VALUE,
+                )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/resource_configuration.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/resource_configuration.py
new file mode 100644
index 00000000..a10d4a66
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/resource_configuration.py
@@ -0,0 +1,98 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import json
+import logging
+from typing import Any, Dict, Optional
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import ResourceConfiguration as RestResourceConfiguration
+from azure.ai.ml.constants._job.job import JobComputePropertyFields
+from azure.ai.ml.entities._mixins import DictMixin, RestTranslatableMixin
+
+module_logger = logging.getLogger(__name__)
+
+
+class ResourceConfiguration(RestTranslatableMixin, DictMixin):
+    """Resource configuration for a job.
+
+    This class should not be instantiated directly. Instead, use its subclasses.
+
+    :keyword instance_count: The number of instances to use for the job.
+    :paramtype instance_count: Optional[int]
+    :keyword instance_type: The type of instance to use for the job.
+    :paramtype instance_type: Optional[str]
+    :keyword properties: The resource's property dictionary.
+    :paramtype properties: Optional[dict[str, Any]]
+    """
+
+    def __init__(
+        self,  # pylint: disable=unused-argument
+        *,
+        instance_count: Optional[int] = None,
+        instance_type: Optional[str] = None,
+        properties: Optional[Dict[str, Any]] = None,
+        **kwargs: Any
+    ) -> None:
+        self.instance_count = instance_count
+        self.instance_type = instance_type
+        self.properties = {}
+        if properties is not None:
+            for key, value in properties.items():
+                if key == JobComputePropertyFields.AISUPERCOMPUTER:
+                    self.properties[JobComputePropertyFields.SINGULARITY.lower()] = value
+                else:
+                    self.properties[key] = value
+
+    def _to_rest_object(self) -> RestResourceConfiguration:
+        serialized_properties = {}
+        if self.properties:
+            for key, value in self.properties.items():
+                try:
+                    if (
+                        key.lower() == JobComputePropertyFields.SINGULARITY.lower()
+                        or key.lower() == JobComputePropertyFields.AISUPERCOMPUTER.lower()
+                    ):
+                        # Map Singularity -> AISupercomputer in SDK until MFE does mapping
+                        key = JobComputePropertyFields.AISUPERCOMPUTER
+                    # recursively convert Ordered Dict to dictionary
+                    serialized_properties[key] = json.loads(json.dumps(value))
+                except Exception:  # pylint: disable=W0718
+                    pass
+        return RestResourceConfiguration(
+            instance_count=self.instance_count,
+            instance_type=self.instance_type,
+            properties=serialized_properties,
+        )
+
+    @classmethod
+    def _from_rest_object(  # pylint: disable=arguments-renamed
+        cls, rest_obj: Optional[RestResourceConfiguration]
+    ) -> Optional["ResourceConfiguration"]:
+        if rest_obj is None:
+            return None
+        return ResourceConfiguration(
+            instance_count=rest_obj.instance_count,
+            instance_type=rest_obj.instance_type,
+            properties=rest_obj.properties,
+            deserialize_properties=True,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, ResourceConfiguration):
+            return NotImplemented
+        return self.instance_count == other.instance_count and self.instance_type == other.instance_type
+
+    def __ne__(self, other: object) -> bool:
+        if not isinstance(other, ResourceConfiguration):
+            return NotImplemented
+        return not self.__eq__(other)
+
+    def _merge_with(self, other: "ResourceConfiguration") -> None:
+        if other:
+            if other.instance_count:
+                self.instance_count = other.instance_count
+            if other.instance_type:
+                self.instance_type = other.instance_type
+            if other.properties:
+                self.properties = other.properties
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/service_instance.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/service_instance.py
new file mode 100644
index 00000000..0e5ba6c6
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/service_instance.py
@@ -0,0 +1,59 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import logging
+from typing import Any, Dict, Optional
+
+from azure.ai.ml._restclient.runhistory.models import ServiceInstanceResult
+from azure.ai.ml.entities._mixins import DictMixin, RestTranslatableMixin
+
+module_logger = logging.getLogger(__name__)
+
+
+class ServiceInstance(RestTranslatableMixin, DictMixin):
+    """Service Instance Result.
+
+    :keyword type: The type of service.
+    :paramtype type: Optional[str]
+    :keyword port: The port used by the service.
+    :paramtype port: Optional[int]
+    :keyword status: The status of the service.
+    :paramtype status: Optional[str]
+    :keyword error: The error message.
+    :paramtype error: Optional[str]
+    :keyword endpoint: The service endpoint.
+    :paramtype endpoint: Optional[str]
+    :keyword properties: The service instance's properties.
+    :paramtype properties: Optional[dict[str, str]]
+    """
+
+    def __init__(
+        self,  # pylint: disable=unused-argument
+        *,
+        type: Optional[str] = None,  # pylint: disable=redefined-builtin
+        port: Optional[int] = None,
+        status: Optional[str] = None,
+        error: Optional[str] = None,
+        endpoint: Optional[str] = None,
+        properties: Optional[Dict[str, str]] = None,
+        **kwargs: Any
+    ) -> None:
+        self.type = type
+        self.port = port
+        self.status = status
+        self.error = error
+        self.endpoint = endpoint
+        self.properties = properties
+
+    @classmethod
+    # pylint: disable=arguments-differ
+    def _from_rest_object(cls, obj: ServiceInstanceResult, node_index: int) -> "ServiceInstance":  # type: ignore
+        return cls(
+            type=obj.type,
+            port=obj.port,
+            status=obj.status,
+            error=obj.error.error.message if obj.error and obj.error.error else None,
+            endpoint=obj.endpoint.replace("<nodeIndex>", str(node_index)) if obj.endpoint else obj.endpoint,
+            properties=obj.properties,
+        )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_helpers.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_helpers.py
new file mode 100644
index 00000000..d3fdf9dc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_helpers.py
@@ -0,0 +1,210 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+# pylint: disable=protected-access
+import re
+from typing import Any
+
+from azure.ai.ml.constants import InputOutputModes
+from azure.ai.ml.constants._component import ComponentJobConstants
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._job.pipeline._io import NodeInput, NodeOutput
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+def _validate_spark_configurations(obj: Any) -> None:
+    # skip validation when component of node is from remote
+    if hasattr(obj, "component") and isinstance(obj.component, str):
+        return
+    if obj.dynamic_allocation_enabled in ["True", "true", True]:
+        if (
+            obj.driver_cores is None
+            or obj.driver_memory is None
+            or obj.executor_cores is None
+            or obj.executor_memory is None
+        ):
+            msg = (
+                "spark.driver.cores, spark.driver.memory, spark.executor.cores and spark.executor.memory are "
+                "mandatory fields."
+            )
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        if obj.dynamic_allocation_min_executors is None or obj.dynamic_allocation_max_executors is None:
+            msg = (
+                "spark.dynamicAllocation.minExecutors and spark.dynamicAllocation.maxExecutors are required "
+                "when dynamic allocation is enabled."
+            )
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        if not (
+            obj.dynamic_allocation_min_executors > 0
+            and obj.dynamic_allocation_min_executors <= obj.dynamic_allocation_max_executors
+        ):
+            msg = (
+                "Dynamic min executors should be bigger than 0 and min executors should be equal or less than "
+                "max executors."
+            )
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        if obj.executor_instances and (
+            obj.executor_instances > obj.dynamic_allocation_max_executors
+            or obj.executor_instances < obj.dynamic_allocation_min_executors
+        ):
+            msg = (
+                "Executor instances must be a valid non-negative integer and must be between "
+                "spark.dynamicAllocation.minExecutors and spark.dynamicAllocation.maxExecutors"
+            )
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+    else:
+        if (
+            obj.driver_cores is None
+            or obj.driver_memory is None
+            or obj.executor_cores is None
+            or obj.executor_memory is None
+            or obj.executor_instances is None
+        ):
+            msg = (
+                "spark.driver.cores, spark.driver.memory, spark.executor.cores, spark.executor.memory and "
+                "spark.executor.instances are mandatory fields."
+            )
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        if obj.dynamic_allocation_min_executors is not None or obj.dynamic_allocation_max_executors is not None:
+            msg = "Should not specify min or max executors when dynamic allocation is disabled."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+
+def _validate_compute_or_resources(compute: Any, resources: Any) -> None:
+    # if resources is set, then ensure it is valid before
+    # checking mutual exclusiveness against compute existence
+    if compute is None and resources is None:
+        msg = "One of either compute or resources must be specified for Spark job"
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.SPARK_JOB,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+    if compute and resources:
+        msg = "Only one of either compute or resources may be specified for Spark job"
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.SPARK_JOB,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+
+# Only "direct" mode is supported for spark job inputs and outputs
+# pylint: disable=no-else-raise, too-many-boolean-expressions
+def _validate_input_output_mode(inputs: Any, outputs: Any) -> None:
+    for input_name, input_value in inputs.items():
+        if isinstance(input_value, Input) and input_value.mode != InputOutputModes.DIRECT:
+            # For standalone job input
+            msg = "Input '{}' is using '{}' mode, only '{}' is supported for Spark job"
+            raise ValidationException(
+                message=msg.format(input_name, input_value.mode, InputOutputModes.DIRECT),
+                no_personal_data_message=msg.format("[input_name]", "[input_value.mode]", "direct"),
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        elif (
+            isinstance(input_value, NodeInput)
+            and (
+                isinstance(input_value._data, Input)
+                and not (
+                    isinstance(input_value._data.path, str)
+                    and bool(re.search(ComponentJobConstants.INPUT_PATTERN, input_value._data.path))
+                )
+                and input_value._data.mode != InputOutputModes.DIRECT
+            )
+            and (isinstance(input_value._meta, Input) and input_value._meta.mode != InputOutputModes.DIRECT)
+        ):
+            # For node input in pipeline job, client side can only validate node input which isn't bound to pipeline
+            # input or node output.
+            # 1. If node input is bound to pipeline input, we can't get pipeline level input mode in node level
+            # validate. Even if we can judge through component input mode (_meta), we should note that pipeline level
+            # input mode has higher priority than component level. so component input can be set "Mount", but it can
+            # run successfully when pipeline input is "Direct".
+            # 2. If node input is bound to last node output, input mode should be decoupled with output mode, so we
+            # always get None mode in node level. In this case, if we define correct "Direct" mode in component yaml,
+            # component level mode will take effect and run successfully. Otherwise, it need to set mode in node level
+            # like input1: path: ${{parent.jobs.sample_word.outputs.output1}} mode: direct.
+            msg = "Input '{}' is using '{}' mode, only '{}' is supported for Spark job"
+            raise ValidationException(
+                message=msg.format(
+                    input_name, input_value._data.mode or input_value._meta.mode, InputOutputModes.DIRECT
+                ),
+                no_personal_data_message=msg.format("[input_name]", "[input_value.mode]", "direct"),
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    for output_name, output_value in outputs.items():
+        if (
+            isinstance(output_value, Output)
+            and output_name != "default"
+            and output_value.mode != InputOutputModes.DIRECT
+        ):
+            # For standalone job output
+            msg = "Output '{}' is using '{}' mode, only '{}' is supported for Spark job"
+            raise ValidationException(
+                message=msg.format(output_name, output_value.mode, InputOutputModes.DIRECT),
+                no_personal_data_message=msg.format("[output_name]", "[output_value.mode]", "direct"),
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        elif (
+            isinstance(output_value, NodeOutput)
+            and output_name != "default"
+            and (
+                isinstance(output_value._data, Output)
+                and not (
+                    isinstance(output_value._data.path, str)
+                    and bool(re.search(ComponentJobConstants.OUTPUT_PATTERN, output_value._data.path))
+                )
+                and output_value._data.mode != InputOutputModes.DIRECT
+            )
+            and (isinstance(output_value._meta, Output) and output_value._meta.mode != InputOutputModes.DIRECT)
+        ):
+            # For node output in pipeline job, client side can only validate node output which isn't bound to pipeline
+            # output.
+            # 1. If node output is bound to pipeline output, we can't get pipeline level output mode in node level
+            # validate. Even if we can judge through component output mode (_meta), we should note that pipeline level
+            # output mode has higher priority than component level. so component output can be set "upload", but it
+            # can run successfully when pipeline output is "Direct".
+            msg = "Output '{}' is using '{}' mode, only '{}' is supported for Spark job"
+            raise ValidationException(
+                message=msg.format(
+                    output_name, output_value._data.mode or output_value._meta.mode, InputOutputModes.DIRECT
+                ),
+                no_personal_data_message=msg.format("[output_name]", "[output_value.mode]", "direct"),
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_job.py
new file mode 100644
index 00000000..10930fb4
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_job.py
@@ -0,0 +1,393 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+# pylint: disable=protected-access, too-many-instance-attributes
+
+import copy
+import logging
+from pathlib import Path
+from typing import TYPE_CHECKING, Any, Dict, Optional, Union
+
+from marshmallow import INCLUDE
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import JobBase
+from azure.ai.ml._restclient.v2023_04_01_preview.models import SparkJob as RestSparkJob
+from azure.ai.ml._schema.job.identity import AMLTokenIdentitySchema, ManagedIdentitySchema, UserIdentitySchema
+from azure.ai.ml._schema.job.parameterized_spark import CONF_KEY_MAP
+from azure.ai.ml._schema.job.spark_job import SparkJobSchema
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE
+from azure.ai.ml.constants._job.job import SparkConfKey
+from azure.ai.ml.entities._credentials import (
+    AmlTokenConfiguration,
+    ManagedIdentityConfiguration,
+    UserIdentityConfiguration,
+    _BaseJobIdentityConfiguration,
+)
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._job._input_output_helpers import (
+    from_rest_data_outputs,
+    from_rest_inputs_to_dataset_literal,
+    to_rest_data_outputs,
+    to_rest_dataset_literal_inputs,
+    validate_inputs_for_args,
+)
+from azure.ai.ml.entities._job.parameterized_spark import ParameterizedSpark
+from azure.ai.ml.entities._util import load_from_dict
+
+from ..._schema import NestedField, UnionField
+from .job import Job
+from .job_io_mixin import JobIOMixin
+from .spark_helpers import _validate_compute_or_resources, _validate_input_output_mode, _validate_spark_configurations
+from .spark_job_entry import SparkJobEntry
+from .spark_job_entry_mixin import SparkJobEntryMixin
+from .spark_resource_configuration import SparkResourceConfiguration
+
+# avoid circular import error
+if TYPE_CHECKING:
+    from azure.ai.ml.entities import SparkComponent
+    from azure.ai.ml.entities._builders import Spark
+
+module_logger = logging.getLogger(__name__)
+
+
+class SparkJob(Job, ParameterizedSpark, JobIOMixin, SparkJobEntryMixin):
+    """A standalone Spark job.
+
+    :keyword driver_cores: The number of cores to use for the driver process, only in cluster mode.
+    :paramtype driver_cores: Optional[int]
+    :keyword driver_memory: The amount of memory to use for the driver process, formatted as strings with a size unit
+        suffix ("k", "m", "g" or "t") (e.g. "512m", "2g").
+    :paramtype driver_memory: Optional[str]
+    :keyword executor_cores: The number of cores to use on each executor.
+    :paramtype executor_cores: Optional[int]
+    :keyword executor_memory: The amount of memory to use per executor process, formatted as strings with a size unit
+        suffix ("k", "m", "g" or "t") (e.g. "512m", "2g").
+    :paramtype executor_memory: Optional[str]
+    :keyword executor_instances: The initial number of executors.
+    :paramtype executor_instances: Optional[int]
+    :keyword dynamic_allocation_enabled: Whether to use dynamic resource allocation, which scales the number of
+        executors registered with this application up and down based on the workload.
+    :paramtype dynamic_allocation_enabled: Optional[bool]
+    :keyword dynamic_allocation_min_executors: The lower bound for the number of executors if dynamic allocation is
+        enabled.
+    :paramtype dynamic_allocation_min_executors: Optional[int]
+    :keyword dynamic_allocation_max_executors: The upper bound for the number of executors if dynamic allocation is
+        enabled.
+    :paramtype dynamic_allocation_max_executors: Optional[int]
+    :keyword inputs: The mapping of input data bindings used in the job.
+    :paramtype inputs: Optional[dict[str, ~azure.ai.ml.Input]]
+    :keyword outputs: The mapping of output data bindings used in the job.
+    :paramtype outputs: Optional[dict[str, ~azure.ai.ml.Output]]
+    :keyword compute: The compute resource the job runs on.
+    :paramtype compute: Optional[str]
+    :keyword identity: The identity that the Spark job will use while running on compute.
+    :paramtype identity: Optional[Union[dict[str, str], ~azure.ai.ml.ManagedIdentityConfiguration,
+        ~azure.ai.ml.AmlTokenConfiguration, ~azure.ai.ml.UserIdentityConfiguration]]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_spark_configurations.py
+            :start-after: [START spark_job_configuration]
+            :end-before: [END spark_job_configuration]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a SparkJob.
+    """
+
+    def __init__(
+        self,
+        *,
+        driver_cores: Optional[Union[int, str]] = None,
+        driver_memory: Optional[str] = None,
+        executor_cores: Optional[Union[int, str]] = None,
+        executor_memory: Optional[str] = None,
+        executor_instances: Optional[Union[int, str]] = None,
+        dynamic_allocation_enabled: Optional[Union[bool, str]] = None,
+        dynamic_allocation_min_executors: Optional[Union[int, str]] = None,
+        dynamic_allocation_max_executors: Optional[Union[int, str]] = None,
+        inputs: Optional[Dict[str, Union[Input, str, bool, int, float]]] = None,
+        outputs: Optional[Dict[str, Output]] = None,
+        compute: Optional[str] = None,
+        identity: Optional[
+            Union[Dict[str, str], ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]
+        ] = None,
+        resources: Optional[Union[Dict, SparkResourceConfiguration]] = None,
+        **kwargs: Any,
+    ) -> None:
+        kwargs[TYPE] = JobType.SPARK
+
+        super().__init__(**kwargs)
+        self.conf: Dict = self.conf or {}
+        self.properties_sparkJob = self.properties or {}
+        self.driver_cores = driver_cores
+        self.driver_memory = driver_memory
+        self.executor_cores = executor_cores
+        self.executor_memory = executor_memory
+        self.executor_instances = executor_instances
+        self.dynamic_allocation_enabled = dynamic_allocation_enabled
+        self.dynamic_allocation_min_executors = dynamic_allocation_min_executors
+        self.dynamic_allocation_max_executors = dynamic_allocation_max_executors
+        self.inputs = inputs  # type: ignore[assignment]
+        self.outputs = outputs  # type: ignore[assignment]
+        self.compute = compute
+        self.resources = resources
+        self.identity = identity
+        if self.executor_instances is None and str(self.dynamic_allocation_enabled).lower() == "true":
+            self.executor_instances = self.dynamic_allocation_min_executors
+
+    @property
+    def resources(self) -> Optional[Union[Dict, SparkResourceConfiguration]]:
+        """The compute resource configuration for the job.
+
+        :return: The compute resource configuration for the job.
+        :rtype: Optional[~azure.ai.ml.entities.SparkResourceConfiguration]
+        """
+        return self._resources
+
+    @resources.setter
+    def resources(self, value: Optional[Union[Dict[str, str], SparkResourceConfiguration]]) -> None:
+        """Sets the compute resource configuration for the job.
+
+        :param value: The compute resource configuration for the job.
+        :type value: Optional[Union[dict[str, str], ~azure.ai.ml.entities.SparkResourceConfiguration]]
+        """
+        if isinstance(value, dict):
+            value = SparkResourceConfiguration(**value)
+        self._resources = value
+
+    @property
+    def identity(
+        self,
+    ) -> Optional[Union[Dict, ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]]:
+        """The identity that the Spark job will use while running on compute.
+
+        :return: The identity that the Spark job will use while running on compute.
+        :rtype: Optional[Union[~azure.ai.ml.ManagedIdentityConfiguration, ~azure.ai.ml.AmlTokenConfiguration,
+            ~azure.ai.ml.UserIdentityConfiguration]]
+        """
+        return self._identity
+
+    @identity.setter
+    def identity(
+        self,
+        value: Optional[
+            Union[Dict[str, str], ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]
+        ],
+    ) -> None:
+        """Sets the identity that the Spark job will use while running on compute.
+
+        :param value: The identity that the Spark job will use while running on compute.
+        :type value: Optional[Union[dict[str, str], ~azure.ai.ml.ManagedIdentityConfiguration,
+            ~azure.ai.ml.AmlTokenConfiguration, ~azure.ai.ml.UserIdentityConfiguration]]
+        """
+        if isinstance(value, dict):
+            identify_schema = UnionField(
+                [
+                    NestedField(ManagedIdentitySchema, unknown=INCLUDE),
+                    NestedField(AMLTokenIdentitySchema, unknown=INCLUDE),
+                    NestedField(UserIdentitySchema, unknown=INCLUDE),
+                ]
+            )
+            value = identify_schema._deserialize(value=value, attr=None, data=None)
+        self._identity = value
+
+    def _to_dict(self) -> Dict:
+        res: dict = SparkJobSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def filter_conf_fields(self) -> Dict[str, str]:
+        """Filters out the fields of the conf attribute that are not among the Spark configuration fields
+        listed in ~azure.ai.ml._schema.job.parameterized_spark.CONF_KEY_MAP and returns them in their own dictionary.
+
+        :return: A dictionary of the conf fields that are not Spark configuration fields.
+        :rtype: dict[str, str]
+        """
+        if self.conf is None:
+            return {}
+        data_conf = {}
+        for conf_key, conf_val in self.conf.items():
+            if not conf_key in CONF_KEY_MAP:
+                data_conf[conf_key] = conf_val
+        return data_conf
+
+    def _to_rest_object(self) -> JobBase:
+        self._validate()
+        conf = {
+            **(self.filter_conf_fields()),
+            "spark.driver.cores": self.driver_cores,
+            "spark.driver.memory": self.driver_memory,
+            "spark.executor.cores": self.executor_cores,
+            "spark.executor.memory": self.executor_memory,
+        }
+        if self.dynamic_allocation_enabled in ["True", "true", True]:
+            conf["spark.dynamicAllocation.enabled"] = True
+            conf["spark.dynamicAllocation.minExecutors"] = self.dynamic_allocation_min_executors
+            conf["spark.dynamicAllocation.maxExecutors"] = self.dynamic_allocation_max_executors
+        if self.executor_instances is not None:
+            conf["spark.executor.instances"] = self.executor_instances
+
+        properties = RestSparkJob(
+            experiment_name=self.experiment_name,
+            display_name=self.display_name,
+            description=self.description,
+            tags=self.tags,
+            code_id=self.code,
+            entry=self.entry._to_rest_object() if self.entry is not None and not isinstance(self.entry, dict) else None,
+            py_files=self.py_files,
+            jars=self.jars,
+            files=self.files,
+            archives=self.archives,
+            identity=(
+                self.identity._to_job_rest_object() if self.identity and not isinstance(self.identity, dict) else None
+            ),
+            conf=conf,
+            properties=self.properties_sparkJob,
+            environment_id=self.environment,
+            inputs=to_rest_dataset_literal_inputs(self.inputs, job_type=self.type),
+            outputs=to_rest_data_outputs(self.outputs),
+            args=self.args,
+            compute_id=self.compute,
+            resources=(
+                self.resources._to_rest_object() if self.resources and not isinstance(self.resources, Dict) else None
+            ),
+        )
+        result = JobBase(properties=properties)
+        result.name = self.name
+        return result
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "SparkJob":
+        loaded_data = load_from_dict(SparkJobSchema, data, context, additional_message, **kwargs)
+        return SparkJob(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_data)
+
+    @classmethod
+    def _load_from_rest(cls, obj: JobBase) -> "SparkJob":
+        rest_spark_job: RestSparkJob = obj.properties
+        rest_spark_conf = copy.copy(rest_spark_job.conf) or {}
+        spark_job = SparkJob(
+            name=obj.name,
+            entry=SparkJobEntry._from_rest_object(rest_spark_job.entry),
+            experiment_name=rest_spark_job.experiment_name,
+            id=obj.id,
+            display_name=rest_spark_job.display_name,
+            description=rest_spark_job.description,
+            tags=rest_spark_job.tags,
+            properties=rest_spark_job.properties,
+            services=rest_spark_job.services,
+            status=rest_spark_job.status,
+            creation_context=obj.system_data,
+            code=rest_spark_job.code_id,
+            compute=rest_spark_job.compute_id,
+            environment=rest_spark_job.environment_id,
+            identity=(
+                _BaseJobIdentityConfiguration._from_rest_object(rest_spark_job.identity)
+                if rest_spark_job.identity
+                else None
+            ),
+            args=rest_spark_job.args,
+            conf=rest_spark_conf,
+            driver_cores=rest_spark_conf.get(
+                SparkConfKey.DRIVER_CORES, None
+            ),  # copy fields from conf into the promote attribute in spark
+            driver_memory=rest_spark_conf.get(SparkConfKey.DRIVER_MEMORY, None),
+            executor_cores=rest_spark_conf.get(SparkConfKey.EXECUTOR_CORES, None),
+            executor_memory=rest_spark_conf.get(SparkConfKey.EXECUTOR_MEMORY, None),
+            executor_instances=rest_spark_conf.get(SparkConfKey.EXECUTOR_INSTANCES, None),
+            dynamic_allocation_enabled=rest_spark_conf.get(SparkConfKey.DYNAMIC_ALLOCATION_ENABLED, None),
+            dynamic_allocation_min_executors=rest_spark_conf.get(SparkConfKey.DYNAMIC_ALLOCATION_MIN_EXECUTORS, None),
+            dynamic_allocation_max_executors=rest_spark_conf.get(SparkConfKey.DYNAMIC_ALLOCATION_MAX_EXECUTORS, None),
+            resources=SparkResourceConfiguration._from_rest_object(rest_spark_job.resources),
+            inputs=from_rest_inputs_to_dataset_literal(rest_spark_job.inputs),
+            outputs=from_rest_data_outputs(rest_spark_job.outputs),
+        )
+        return spark_job
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> "SparkComponent":
+        """Translate a spark job to component.
+
+        :param context: Context of spark job YAML file.
+        :type context: dict
+        :return: Translated spark component.
+        :rtype: SparkComponent
+        """
+        from azure.ai.ml.entities import SparkComponent
+
+        pipeline_job_dict = kwargs.get("pipeline_job_dict", {})
+        context = context or {BASE_PATH_CONTEXT_KEY: Path("./")}
+
+        # Create anonymous spark component with default version as 1
+        return SparkComponent(
+            tags=self.tags,
+            is_anonymous=True,
+            base_path=context[BASE_PATH_CONTEXT_KEY],
+            description=self.description,
+            code=self.code,
+            entry=self.entry,
+            py_files=self.py_files,
+            jars=self.jars,
+            files=self.files,
+            archives=self.archives,
+            driver_cores=self.driver_cores,
+            driver_memory=self.driver_memory,
+            executor_cores=self.executor_cores,
+            executor_memory=self.executor_memory,
+            executor_instances=self.executor_instances,
+            dynamic_allocation_enabled=self.dynamic_allocation_enabled,
+            dynamic_allocation_min_executors=self.dynamic_allocation_min_executors,
+            dynamic_allocation_max_executors=self.dynamic_allocation_max_executors,
+            conf=self.conf,
+            properties=self.properties_sparkJob,
+            environment=self.environment,
+            inputs=self._to_inputs(inputs=self.inputs, pipeline_job_dict=pipeline_job_dict),
+            outputs=self._to_outputs(outputs=self.outputs, pipeline_job_dict=pipeline_job_dict),
+            args=self.args,
+        )
+
+    def _to_node(self, context: Optional[Dict] = None, **kwargs: Any) -> "Spark":
+        """Translate a spark job to a pipeline node.
+
+        :param context: Context of spark job YAML file.
+        :type context: dict
+        :return: Translated spark component.
+        :rtype: Spark
+        """
+        from azure.ai.ml.entities._builders import Spark
+
+        component = self._to_component(context, **kwargs)
+
+        return Spark(
+            display_name=self.display_name,
+            description=self.description,
+            tags=self.tags,
+            # code, entry, py_files, jars, files, archives, environment and args are static and not allowed to be
+            # overwritten. And we will always get them from component.
+            component=component,
+            identity=self.identity,
+            driver_cores=self.driver_cores,
+            driver_memory=self.driver_memory,
+            executor_cores=self.executor_cores,
+            executor_memory=self.executor_memory,
+            executor_instances=self.executor_instances,
+            dynamic_allocation_enabled=self.dynamic_allocation_enabled,
+            dynamic_allocation_min_executors=self.dynamic_allocation_min_executors,
+            dynamic_allocation_max_executors=self.dynamic_allocation_max_executors,
+            conf=self.conf,
+            inputs=self.inputs,  # type: ignore[arg-type]
+            outputs=self.outputs,  # type: ignore[arg-type]
+            compute=self.compute,
+            resources=self.resources,
+            properties=self.properties_sparkJob,
+        )
+
+    def _validate(self) -> None:
+        # TODO: make spark job schema validatable?
+        if self.resources and not isinstance(self.resources, Dict):
+            self.resources._validate()
+        _validate_compute_or_resources(self.compute, self.resources)
+        _validate_input_output_mode(self.inputs, self.outputs)
+        _validate_spark_configurations(self)
+        self._validate_entry()
+
+        if self.args:
+            validate_inputs_for_args(self.args, self.inputs)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_job_entry.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_job_entry.py
new file mode 100644
index 00000000..ed8d3ca7
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_job_entry.py
@@ -0,0 +1,59 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+# pylint: disable=redefined-builtin
+
+from typing import Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import SparkJobEntry as RestSparkJobEntry
+from azure.ai.ml._restclient.v2023_04_01_preview.models import SparkJobPythonEntry, SparkJobScalaEntry
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class SparkJobEntryType:
+    """Type of Spark job entry. Possibilities are Python file entry or Scala class entry."""
+
+    SPARK_JOB_FILE_ENTRY = "SparkJobPythonEntry"
+    SPARK_JOB_CLASS_ENTRY = "SparkJobScalaEntry"
+
+
+class SparkJobEntry(RestTranslatableMixin):
+    """Entry for Spark job.
+
+    :keyword entry: The file or class entry point.
+    :paramtype entry: str
+    :keyword type: The entry type. Accepted values are SparkJobEntryType.SPARK_JOB_FILE_ENTRY or
+        SparkJobEntryType.SPARK_JOB_CLASS_ENTRY. Defaults to SparkJobEntryType.SPARK_JOB_FILE_ENTRY.
+    :paramtype type: ~azure.ai.ml.entities.SparkJobEntryType
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_spark_configurations.py
+            :start-after: [START spark_component_definition]
+            :end-before: [END spark_component_definition]
+            :language: python
+            :dedent: 8
+            :caption: Creating SparkComponent.
+    """
+
+    def __init__(self, *, entry: str, type: str = SparkJobEntryType.SPARK_JOB_FILE_ENTRY) -> None:
+        self.entry_type = type
+        self.entry = entry
+
+    @classmethod
+    def _from_rest_object(cls, obj: Union[SparkJobPythonEntry, SparkJobScalaEntry]) -> Optional["SparkJobEntry"]:
+        if obj is None:
+            return None
+        if isinstance(obj, dict):
+            obj = RestSparkJobEntry.from_dict(obj)
+        if obj.spark_job_entry_type == SparkJobEntryType.SPARK_JOB_FILE_ENTRY:
+            return SparkJobEntry(
+                entry=obj.__dict__.get("file", None),
+                type=SparkJobEntryType.SPARK_JOB_FILE_ENTRY,
+            )
+        return SparkJobEntry(entry=obj.class_name, type=SparkJobEntryType.SPARK_JOB_CLASS_ENTRY)
+
+    def _to_rest_object(self) -> Union[SparkJobPythonEntry, SparkJobScalaEntry]:
+        if self.entry_type == SparkJobEntryType.SPARK_JOB_FILE_ENTRY:
+            return SparkJobPythonEntry(file=self.entry)
+        return SparkJobScalaEntry(class_name=self.entry)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_job_entry_mixin.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_job_entry_mixin.py
new file mode 100644
index 00000000..2a1ff549
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_job_entry_mixin.py
@@ -0,0 +1,64 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+import re
+from typing import Any, Dict, Optional, Union, cast
+
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+from .spark_job_entry import SparkJobEntry, SparkJobEntryType
+
+
+class SparkJobEntryMixin:
+    CODE_ID_RE_PATTERN = re.compile(
+        (
+            r"\/subscriptions\/(?P<subscription>[\w,-]+)\/resourceGroups\/(?P<resource_group>[\w,-]+)"
+            r"\/providers\/Microsoft\.MachineLearningServices\/workspaces\/(?P<workspace>[\w,-]+)"
+            r"\/codes\/(?P<code_id>[\w,-]+)"  # fmt: skip
+        )
+    )
+
+    def __init__(self, **kwargs: Any):
+        self._entry = None
+        self.entry = kwargs.get("entry", None)
+
+    @property
+    def entry(self) -> Optional[Union[Dict[str, str], SparkJobEntry]]:
+        return self._entry
+
+    @entry.setter
+    def entry(self, value: Optional[Union[Dict[str, str], SparkJobEntry]]) -> None:
+        if isinstance(value, dict):
+            if value.get("file", None):
+                _entry = cast(str, value.get("file"))
+                self._entry = SparkJobEntry(entry=_entry, type=SparkJobEntryType.SPARK_JOB_FILE_ENTRY)
+                return
+            if value.get("class_name", None):
+                _entry = cast(str, value.get("class_name"))
+                self._entry = SparkJobEntry(entry=_entry, type=SparkJobEntryType.SPARK_JOB_CLASS_ENTRY)
+                return
+        self._entry = value
+
+    def _validate_entry(self) -> None:
+        if self.entry is None:
+            # Entry is a required field for local component and when we load a remote job, component now is an arm_id,
+            # entry is from node level returned from service. Entry is only None when we reference an existing
+            # component with a function and the referenced component is in remote with name and version.
+            return
+        if not isinstance(self.entry, SparkJobEntry):
+            msg = f"Unsupported type {type(self.entry)} detected when validate entry, entry should be SparkJobEntry."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        if self.entry.entry_type == SparkJobEntryType.SPARK_JOB_CLASS_ENTRY:
+            msg = "Classpath is not supported, please use 'file' to define the entry file."
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_resource_configuration.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_resource_configuration.py
new file mode 100644
index 00000000..138fc7ed
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/spark_resource_configuration.py
@@ -0,0 +1,91 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import Optional, Union
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    SparkResourceConfiguration as RestSparkResourceConfiguration,
+)
+from azure.ai.ml.entities._mixins import DictMixin, RestTranslatableMixin
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationException
+
+
+class SparkResourceConfiguration(RestTranslatableMixin, DictMixin):
+    """Compute resource configuration for Spark component or job.
+
+    :keyword instance_type: The type of VM to be used by the compute target.
+    :paramtype instance_type: Optional[str]
+    :keyword runtime_version: The Spark runtime version.
+    :paramtype runtime_version: Optional[str]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_spark_configurations.py
+            :start-after: [START spark_resource_configuration]
+            :end-before: [END spark_resource_configuration]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a SparkJob with SparkResourceConfiguration.
+    """
+
+    instance_type_list = [
+        "standard_e4s_v3",
+        "standard_e8s_v3",
+        "standard_e16s_v3",
+        "standard_e32s_v3",
+        "standard_e64s_v3",
+    ]
+
+    def __init__(self, *, instance_type: Optional[str] = None, runtime_version: Optional[str] = None) -> None:
+        self.instance_type = instance_type
+        self.runtime_version = runtime_version
+
+    def _to_rest_object(self) -> RestSparkResourceConfiguration:
+        return RestSparkResourceConfiguration(instance_type=self.instance_type, runtime_version=self.runtime_version)
+
+    @classmethod
+    def _from_rest_object(
+        cls, obj: Union[dict, None, RestSparkResourceConfiguration]
+    ) -> Optional["SparkResourceConfiguration"]:
+        if obj is None:
+            return None
+        if isinstance(obj, dict):
+            return SparkResourceConfiguration(**obj)
+        return SparkResourceConfiguration(instance_type=obj.instance_type, runtime_version=obj.runtime_version)
+
+    def _validate(self) -> None:
+        # TODO: below logic is duplicated to SparkResourceConfigurationSchema, maybe make SparkJob schema validatable
+        if self.instance_type is None or self.instance_type == "":
+            msg = "Instance type must be specified for SparkResourceConfiguration"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+        if self.instance_type.lower() not in self.instance_type_list:
+            msg = "Instance type must be specified for the list of {}".format(",".join(self.instance_type_list))
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SPARK_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+            )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, SparkResourceConfiguration):
+            return NotImplemented
+        return self.instance_type == other.instance_type and self.runtime_version == other.runtime_version
+
+    def __ne__(self, other: object) -> bool:
+        if not isinstance(other, SparkResourceConfiguration):
+            return NotImplemented
+        return not self.__eq__(other)
+
+    def _merge_with(self, other: "SparkResourceConfiguration") -> None:
+        if other:
+            if other.instance_type:
+                self.instance_type = other.instance_type
+            if other.runtime_version:
+                self.runtime_version = other.runtime_version
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/__init__.py
new file mode 100644
index 00000000..fdf8caba
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/__init__.py
@@ -0,0 +1,5 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/early_termination_policy.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/early_termination_policy.py
new file mode 100644
index 00000000..b1b928fc
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/early_termination_policy.py
@@ -0,0 +1,191 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from abc import ABC
+from typing import Any, Optional, cast
+
+from azure.ai.ml._restclient.v2023_04_01_preview.models import BanditPolicy as RestBanditPolicy
+from azure.ai.ml._restclient.v2023_04_01_preview.models import EarlyTerminationPolicy as RestEarlyTerminationPolicy
+from azure.ai.ml._restclient.v2023_04_01_preview.models import EarlyTerminationPolicyType
+from azure.ai.ml._restclient.v2023_04_01_preview.models import MedianStoppingPolicy as RestMedianStoppingPolicy
+from azure.ai.ml._restclient.v2023_04_01_preview.models import (
+    TruncationSelectionPolicy as RestTruncationSelectionPolicy,
+)
+from azure.ai.ml._utils.utils import camel_to_snake
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class EarlyTerminationPolicy(ABC, RestTranslatableMixin):
+    def __init__(
+        self,
+        *,
+        delay_evaluation: int,
+        evaluation_interval: int,
+    ):
+        self.type = None
+        self.delay_evaluation = delay_evaluation
+        self.evaluation_interval = evaluation_interval
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestEarlyTerminationPolicy) -> Optional["EarlyTerminationPolicy"]:
+        if not obj:
+            return None
+
+        policy: Any = None
+        if obj.policy_type == EarlyTerminationPolicyType.BANDIT:
+            policy = BanditPolicy._from_rest_object(obj)  # pylint: disable=protected-access
+
+        if obj.policy_type == EarlyTerminationPolicyType.MEDIAN_STOPPING:
+            policy = MedianStoppingPolicy._from_rest_object(obj)  # pylint: disable=protected-access
+
+        if obj.policy_type == EarlyTerminationPolicyType.TRUNCATION_SELECTION:
+            policy = TruncationSelectionPolicy._from_rest_object(obj)  # pylint: disable=protected-access
+
+        return cast(Optional["EarlyTerminationPolicy"], policy)
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, EarlyTerminationPolicy):
+            raise NotImplementedError
+        res: bool = self._to_rest_object() == other._to_rest_object()
+        return res
+
+
+class BanditPolicy(EarlyTerminationPolicy):
+    """Defines an early termination policy based on slack criteria and a frequency and delay interval for evaluation.
+
+    :keyword delay_evaluation: Number of intervals by which to delay the first evaluation. Defaults to 0.
+    :paramtype delay_evaluation: int
+    :keyword evaluation_interval: Interval (number of runs) between policy evaluations. Defaults to 0.
+    :paramtype evaluation_interval: int
+    :keyword slack_amount: Absolute distance allowed from the best performing run. Defaults to 0.
+    :paramtype slack_amount: float
+    :keyword slack_factor: Ratio of the allowed distance from the best performing run. Defaults to 0.
+    :paramtype slack_factor: float
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_bandit_policy]
+            :end-before: [END configure_sweep_job_bandit_policy]
+            :language: python
+            :dedent: 8
+            :caption: Configuring BanditPolicy early termination of a hyperparameter sweep on a Command job.
+    """
+
+    def __init__(
+        self,
+        *,
+        delay_evaluation: int = 0,
+        evaluation_interval: int = 0,
+        slack_amount: float = 0,
+        slack_factor: float = 0,
+    ) -> None:
+        super().__init__(delay_evaluation=delay_evaluation, evaluation_interval=evaluation_interval)
+        self.type = EarlyTerminationPolicyType.BANDIT.lower()
+        self.slack_factor = slack_factor
+        self.slack_amount = slack_amount
+
+    def _to_rest_object(self) -> RestBanditPolicy:
+        return RestBanditPolicy(
+            delay_evaluation=self.delay_evaluation,
+            evaluation_interval=self.evaluation_interval,
+            slack_factor=self.slack_factor,
+            slack_amount=self.slack_amount,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestBanditPolicy) -> "BanditPolicy":
+        return cls(
+            delay_evaluation=obj.delay_evaluation,
+            evaluation_interval=obj.evaluation_interval,
+            slack_factor=obj.slack_factor,
+            slack_amount=obj.slack_amount,
+        )
+
+
+class MedianStoppingPolicy(EarlyTerminationPolicy):
+    """Defines an early termination policy based on a running average of the primary metric of all runs.
+
+    :keyword delay_evaluation: Number of intervals by which to delay the first evaluation. Defaults to 0.
+    :paramtype delay_evaluation: int
+    :keyword evaluation_interval: Interval (number of runs) between policy evaluations. Defaults to 1.
+    :paramtype evaluation_interval: int
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_median_stopping_policy]
+            :end-before: [END configure_sweep_job_median_stopping_policy]
+            :language: python
+            :dedent: 8
+            :caption: Configuring an early termination policy for a hyperparameter sweep job using MedianStoppingPolicy
+    """
+
+    def __init__(
+        self,
+        *,
+        delay_evaluation: int = 0,
+        evaluation_interval: int = 1,
+    ) -> None:
+        super().__init__(delay_evaluation=delay_evaluation, evaluation_interval=evaluation_interval)
+        self.type = camel_to_snake(EarlyTerminationPolicyType.MEDIAN_STOPPING)
+
+    def _to_rest_object(self) -> RestMedianStoppingPolicy:
+        return RestMedianStoppingPolicy(
+            delay_evaluation=self.delay_evaluation, evaluation_interval=self.evaluation_interval
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestMedianStoppingPolicy) -> "MedianStoppingPolicy":
+        return cls(
+            delay_evaluation=obj.delay_evaluation,
+            evaluation_interval=obj.evaluation_interval,
+        )
+
+
+class TruncationSelectionPolicy(EarlyTerminationPolicy):
+    """Defines an early termination policy that cancels a given percentage of runs at each evaluation interval.
+
+    :keyword delay_evaluation: Number of intervals by which to delay the first evaluation. Defaults to 0.
+    :paramtype delay_evaluation: int
+    :keyword evaluation_interval: Interval (number of runs) between policy evaluations. Defaults to 0.
+    :paramtype evaluation_interval: int
+    :keyword truncation_percentage: The percentage of runs to cancel at each evaluation interval. Defaults to 0.
+    :paramtype truncation_percentage: int
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_truncation_selection_policy]
+            :end-before: [END configure_sweep_job_truncation_selection_policy]
+            :language: python
+            :dedent: 8
+            :caption: Configuring an early termination policy for a hyperparameter sweep job
+                using TruncationStoppingPolicy
+    """
+
+    def __init__(
+        self,
+        *,
+        delay_evaluation: int = 0,
+        evaluation_interval: int = 0,
+        truncation_percentage: int = 0,
+    ) -> None:
+        super().__init__(delay_evaluation=delay_evaluation, evaluation_interval=evaluation_interval)
+        self.type = camel_to_snake(EarlyTerminationPolicyType.TRUNCATION_SELECTION)
+        self.truncation_percentage = truncation_percentage
+
+    def _to_rest_object(self) -> RestTruncationSelectionPolicy:
+        return RestTruncationSelectionPolicy(
+            delay_evaluation=self.delay_evaluation,
+            evaluation_interval=self.evaluation_interval,
+            truncation_percentage=self.truncation_percentage,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestTruncationSelectionPolicy) -> "TruncationSelectionPolicy":
+        return cls(
+            delay_evaluation=obj.delay_evaluation,
+            evaluation_interval=obj.evaluation_interval,
+            truncation_percentage=obj.truncation_percentage,
+        )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/objective.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/objective.py
new file mode 100644
index 00000000..45e13332
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/objective.py
@@ -0,0 +1,53 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from typing import Optional
+
+from azure.ai.ml._restclient.v2023_08_01_preview.models import Objective as RestObjective
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class Objective(RestTranslatableMixin):
+    """Optimization objective.
+
+    :param goal: Defines supported metric goals for hyperparameter tuning. Accepted values
+     are: "minimize", "maximize".
+    :type goal: str
+    :param primary_metric: The name of the metric to optimize.
+    :type primary_metric: str
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_bayesian_sampling_algorithm]
+            :end-before: [END configure_sweep_job_bayesian_sampling_algorithm]
+            :language: python
+            :dedent: 8
+            :caption: Assigning an objective to a SweepJob.
+    """
+
+    def __init__(self, goal: Optional[str], primary_metric: Optional[str] = None) -> None:
+        """Optimization objective.
+
+        :param goal: Defines supported metric goals for hyperparameter tuning. Acceptable values
+            are: "minimize" or "maximize".
+        :type goal: str
+        :param primary_metric: The name of the metric to optimize.
+        :type primary_metric: str
+        """
+        if goal is not None:
+            self.goal = goal.lower()
+        self.primary_metric = primary_metric
+
+    def _to_rest_object(self) -> RestObjective:
+        return RestObjective(
+            goal=self.goal,
+            primary_metric=self.primary_metric,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestObjective) -> Optional["Objective"]:
+        if not obj:
+            return None
+
+        return cls(goal=obj.goal, primary_metric=obj.primary_metric)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/parameterized_sweep.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/parameterized_sweep.py
new file mode 100644
index 00000000..5d69201f
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/parameterized_sweep.py
@@ -0,0 +1,341 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from typing import Any, Dict, List, Optional, Type, Union
+
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, ValidationErrorType, ValidationException
+
+from ..job_limits import SweepJobLimits
+from ..job_resource_configuration import JobResourceConfiguration
+from ..queue_settings import QueueSettings
+from .early_termination_policy import (
+    BanditPolicy,
+    EarlyTerminationPolicy,
+    EarlyTerminationPolicyType,
+    MedianStoppingPolicy,
+    TruncationSelectionPolicy,
+)
+from .objective import Objective
+from .sampling_algorithm import (
+    BayesianSamplingAlgorithm,
+    GridSamplingAlgorithm,
+    RandomSamplingAlgorithm,
+    RestBayesianSamplingAlgorithm,
+    RestGridSamplingAlgorithm,
+    RestRandomSamplingAlgorithm,
+    RestSamplingAlgorithm,
+    SamplingAlgorithm,
+    SamplingAlgorithmType,
+)
+
+SAMPLING_ALGORITHM_TO_REST_CONSTRUCTOR: Dict[SamplingAlgorithmType, Type[RestSamplingAlgorithm]] = {
+    SamplingAlgorithmType.RANDOM: RestRandomSamplingAlgorithm,
+    SamplingAlgorithmType.GRID: RestGridSamplingAlgorithm,
+    SamplingAlgorithmType.BAYESIAN: RestBayesianSamplingAlgorithm,
+}
+
+SAMPLING_ALGORITHM_CONSTRUCTOR: Dict[SamplingAlgorithmType, Type[SamplingAlgorithm]] = {
+    SamplingAlgorithmType.RANDOM: RandomSamplingAlgorithm,
+    SamplingAlgorithmType.GRID: GridSamplingAlgorithm,
+    SamplingAlgorithmType.BAYESIAN: BayesianSamplingAlgorithm,
+}
+
+
+class ParameterizedSweep:  # pylint:disable=too-many-instance-attributes
+    """Shared logic for standalone and pipeline sweep job."""
+
+    def __init__(
+        self,
+        limits: Optional[SweepJobLimits] = None,
+        sampling_algorithm: Optional[Union[str, SamplingAlgorithm]] = None,
+        objective: Optional[Union[Dict, Objective]] = None,
+        early_termination: Optional[Any] = None,
+        search_space: Optional[Dict] = None,
+        queue_settings: Optional[QueueSettings] = None,
+        resources: Optional[Union[dict, JobResourceConfiguration]] = None,
+    ) -> None:
+        """
+        :param limits: Limits for sweep job.
+        :type limits: ~azure.ai.ml.sweep.SweepJobLimits
+        :param sampling_algorithm: Sampling algorithm for sweep job.
+        :type sampling_algorithm: ~azure.ai.ml.sweep.SamplingAlgorithm
+        :param objective: Objective for sweep job.
+        :type objective: ~azure.ai.ml.sweep.Objective
+        :param early_termination: Early termination policy for sweep job.
+        :type early_termination: ~azure.ai.ml.entities._job.sweep.early_termination_policy.EarlyTerminationPolicy
+        :param search_space: Search space for sweep job.
+        :type search_space: Dict[str, Union[
+            ~azure.ai.ml.sweep.Choice,
+            ~azure.ai.ml.sweep.LogNormal,
+            ~azure.ai.ml.sweep.LogUniform,
+            ~azure.ai.ml.sweep.Normal,
+            ~azure.ai.ml.sweep.QLogNormal,
+            ~azure.ai.ml.sweep.QLogUniform,
+            ~azure.ai.ml.sweep.QNormal,
+            ~azure.ai.ml.sweep.QUniform,
+            ~azure.ai.ml.sweep.Randint,
+            ~azure.ai.ml.sweep.Uniform
+
+            ]]
+        :param queue_settings: Queue settings for sweep job.
+        :type queue_settings: ~azure.ai.ml.entities.QueueSettings
+        :param resources: Compute Resource configuration for the job.
+        :type resources: ~azure.ai.ml.entities.ResourceConfiguration
+        """
+        self.sampling_algorithm = sampling_algorithm
+        self.early_termination = early_termination  # type: ignore[assignment]
+        self._limits = limits
+        self.search_space = search_space
+        self.queue_settings = queue_settings
+        self.objective: Optional[Objective] = None
+        self.resources = resources
+
+        if isinstance(objective, Dict):
+            self.objective = Objective(**objective)
+        else:
+            self.objective = objective
+
+    @property
+    def resources(self) -> Optional[Union[dict, JobResourceConfiguration]]:
+        """Resources for sweep job.
+
+        :returns: Resources for sweep job.
+        :rtype: ~azure.ai.ml.entities.ResourceConfiguration
+        """
+        return self._resources
+
+    @resources.setter
+    def resources(self, value: Optional[Union[dict, JobResourceConfiguration]]) -> None:
+        """Set Resources for sweep job.
+
+        :param value: Compute Resource configuration for the job.
+        :type value: ~azure.ai.ml.entities.ResourceConfiguration
+        """
+        if isinstance(value, dict):
+            value = JobResourceConfiguration(**value)
+        self._resources = value
+
+    @property
+    def limits(self) -> Optional[SweepJobLimits]:
+        """Limits for sweep job.
+
+        :returns: Limits for sweep job.
+        :rtype: ~azure.ai.ml.sweep.SweepJobLimits
+        """
+        return self._limits
+
+    @limits.setter
+    def limits(self, value: SweepJobLimits) -> None:
+        """Set limits for sweep job.
+
+        :param value: Limits for sweep job.
+        :type value: ~azure.ai.ml.sweep.SweepJobLimits
+        """
+        if not isinstance(value, SweepJobLimits):
+            msg = f"limits must be SweepJobLimits but get {type(value)} instead"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SWEEP_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.INVALID_VALUE,
+            )
+        self._limits = value
+
+    def set_resources(
+        self,
+        *,
+        instance_type: Optional[Union[str, List[str]]] = None,
+        instance_count: Optional[int] = None,
+        locations: Optional[List[str]] = None,
+        properties: Optional[Dict] = None,
+        docker_args: Optional[str] = None,
+        shm_size: Optional[str] = None,
+    ) -> None:
+        """Set resources for Sweep.
+
+        :keyword instance_type: The instance type to use for the job.
+        :paramtype instance_type: Optional[Union[str, List[str]]]
+        :keyword instance_count: The number of instances to use for the job.
+        :paramtype instance_count: Optional[int]
+        :keyword locations: The locations to use for the job.
+        :paramtype locations: Optional[List[str]]
+        :keyword properties: The properties for the job.
+        :paramtype properties: Optional[Dict]
+        :keyword docker_args: The docker arguments for the job.
+        :paramtype docker_args: Optional[str]
+        :keyword shm_size: The shared memory size for the job.
+        :paramtype shm_size: Optional[str]
+        """
+        if self.resources is None:
+            self.resources = JobResourceConfiguration()
+
+        if not isinstance(self.resources, dict):
+            if locations is not None:
+                self.resources.locations = locations
+            if instance_type is not None:
+                self.resources.instance_type = instance_type
+            if instance_count is not None:
+                self.resources.instance_count = instance_count
+            if properties is not None:
+                self.resources.properties = properties
+            if docker_args is not None:
+                self.resources.docker_args = docker_args
+            if shm_size is not None:
+                self.resources.shm_size = shm_size
+
+    def set_limits(
+        self,
+        *,
+        max_concurrent_trials: Optional[int] = None,
+        max_total_trials: Optional[int] = None,
+        timeout: Optional[int] = None,
+        trial_timeout: Optional[int] = None,
+    ) -> None:
+        """Set limits for Sweep node. Leave parameters as None if you don't want to update corresponding values.
+
+        :keyword max_concurrent_trials: maximum concurrent trial number.
+        :paramtype max_concurrent_trials: int
+        :keyword max_total_trials: maximum total trial number.
+        :paramtype max_total_trials: int
+        :keyword timeout: total timeout in seconds for sweep node
+        :paramtype timeout: int
+        :keyword trial_timeout: timeout in seconds for each trial
+        :paramtype trial_timeout: int
+        """
+        # Looks related to https://github.com/pylint-dev/pylint/issues/3502, still an open issue
+        # pylint:disable=attribute-defined-outside-init
+        if self._limits is None:
+            self._limits = SweepJobLimits(
+                max_concurrent_trials=max_concurrent_trials,
+                max_total_trials=max_total_trials,
+                timeout=timeout,
+                trial_timeout=trial_timeout,
+            )
+        else:
+            if self.limits is not None:
+                if max_concurrent_trials is not None:
+                    self.limits.max_concurrent_trials = max_concurrent_trials
+                if max_total_trials is not None:
+                    self.limits.max_total_trials = max_total_trials
+                if timeout is not None:
+                    self.limits.timeout = timeout
+                if trial_timeout is not None:
+                    self.limits.trial_timeout = trial_timeout
+
+    def set_objective(self, *, goal: Optional[str] = None, primary_metric: Optional[str] = None) -> None:
+        """Set the sweep object.. Leave parameters as None if you don't want to update corresponding values.
+
+        :keyword goal: Defines supported metric goals for hyperparameter tuning. Acceptable values are:
+            "minimize" and "maximize".
+        :paramtype goal: str
+        :keyword primary_metric: Name of the metric to optimize.
+        :paramtype primary_metric: str
+        """
+
+        if self.objective is not None:
+            if goal:
+                self.objective.goal = goal
+            if primary_metric:
+                self.objective.primary_metric = primary_metric
+        else:
+            self.objective = Objective(goal=goal, primary_metric=primary_metric)
+
+    @property
+    def sampling_algorithm(self) -> Optional[Union[str, SamplingAlgorithm]]:
+        """Sampling algorithm for sweep job.
+
+        :returns: Sampling algorithm for sweep job.
+        :rtype: ~azure.ai.ml.sweep.SamplingAlgorithm
+        """
+        return self._sampling_algorithm
+
+    @sampling_algorithm.setter
+    def sampling_algorithm(self, value: Optional[Union[SamplingAlgorithm, str]] = None) -> None:
+        """Set sampling algorithm for sweep job.
+
+        :param value: Sampling algorithm for sweep job.
+        :type value: ~azure.ai.ml.sweep.SamplingAlgorithm
+        """
+        if value is None:
+            self._sampling_algorithm = None
+        elif isinstance(value, SamplingAlgorithm) or (
+            isinstance(value, str) and value.lower().capitalize() in SAMPLING_ALGORITHM_CONSTRUCTOR
+        ):
+            self._sampling_algorithm = value
+        else:
+            msg = f"unsupported sampling algorithm: {value}"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SWEEP_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.INVALID_VALUE,
+            )
+
+    def _get_rest_sampling_algorithm(self) -> RestSamplingAlgorithm:
+        # TODO: self.sampling_algorithm will always return SamplingAlgorithm
+        if isinstance(self.sampling_algorithm, SamplingAlgorithm):
+            return self.sampling_algorithm._to_rest_object()  # pylint: disable=protected-access
+
+        if isinstance(self.sampling_algorithm, str):
+            return SAMPLING_ALGORITHM_CONSTRUCTOR[  # pylint: disable=protected-access
+                SamplingAlgorithmType(self.sampling_algorithm.lower().capitalize())
+            ]()._to_rest_object()
+
+        msg = f"Received unsupported value {self._sampling_algorithm} as the sampling algorithm"
+        raise ValidationException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.SWEEP_JOB,
+            error_category=ErrorCategory.USER_ERROR,
+            error_type=ValidationErrorType.INVALID_VALUE,
+        )
+
+    @property
+    def early_termination(self) -> Optional[Union[str, EarlyTerminationPolicy]]:
+        """Early termination policy for sweep job.
+
+        :returns: Early termination policy for sweep job.
+        :rtype: ~azure.ai.ml.entities._job.sweep.early_termination_policy.EarlyTerminationPolicy
+        """
+        return self._early_termination
+
+    @early_termination.setter
+    def early_termination(self, value: Any) -> None:
+        """Set early termination policy for sweep job.
+
+        :param value: Early termination policy for sweep job.
+        :type value: ~azure.ai.ml.entities._job.sweep.early_termination_policy.EarlyTerminationPolicy
+        """
+        self._early_termination: Optional[Union[str, EarlyTerminationPolicy]]
+        if value is None:
+            self._early_termination = None
+        elif isinstance(value, EarlyTerminationPolicy):
+            self._early_termination = value
+        elif isinstance(value, str):
+            value = value.lower().capitalize()
+            if value == EarlyTerminationPolicyType.BANDIT:
+                self._early_termination = BanditPolicy()
+            elif value == EarlyTerminationPolicyType.MEDIAN_STOPPING:
+                self._early_termination = MedianStoppingPolicy()
+            elif value == EarlyTerminationPolicyType.TRUNCATION_SELECTION:
+                self._early_termination = TruncationSelectionPolicy()
+            else:
+                msg = f"Received unsupported value {value} as the early termination policy"
+                raise ValidationException(
+                    message=msg,
+                    no_personal_data_message=msg,
+                    target=ErrorTarget.SWEEP_JOB,
+                    error_category=ErrorCategory.USER_ERROR,
+                    error_type=ValidationErrorType.INVALID_VALUE,
+                )
+        else:
+            msg = f"Received unsupported value of type {type(value)} as the early termination policy"
+            raise ValidationException(
+                message=msg,
+                no_personal_data_message=msg,
+                target=ErrorTarget.SWEEP_JOB,
+                error_category=ErrorCategory.USER_ERROR,
+                error_type=ValidationErrorType.INVALID_VALUE,
+            )
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/sampling_algorithm.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/sampling_algorithm.py
new file mode 100644
index 00000000..d0bf795d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/sampling_algorithm.py
@@ -0,0 +1,141 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+from abc import ABC
+from typing import Any, Optional, Union, cast
+
+from azure.ai.ml._restclient.v2023_08_01_preview.models import (
+    BayesianSamplingAlgorithm as RestBayesianSamplingAlgorithm,
+)
+from azure.ai.ml._restclient.v2023_08_01_preview.models import GridSamplingAlgorithm as RestGridSamplingAlgorithm
+from azure.ai.ml._restclient.v2023_08_01_preview.models import RandomSamplingAlgorithm as RestRandomSamplingAlgorithm
+from azure.ai.ml._restclient.v2023_08_01_preview.models import SamplingAlgorithm as RestSamplingAlgorithm
+from azure.ai.ml._restclient.v2023_08_01_preview.models import SamplingAlgorithmType
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+
+
+class SamplingAlgorithm(ABC, RestTranslatableMixin):
+    """Base class for sampling algorithms.
+
+    This class should not be instantiated directly. Instead, use one of its subclasses.
+    """
+
+    def __init__(self) -> None:
+        self.type = None
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestSamplingAlgorithm) -> Optional["SamplingAlgorithm"]:
+        if not obj:
+            return None
+
+        sampling_algorithm: Any = None
+        if obj.sampling_algorithm_type == SamplingAlgorithmType.RANDOM:
+            sampling_algorithm = RandomSamplingAlgorithm._from_rest_object(obj)  # pylint: disable=protected-access
+
+        if obj.sampling_algorithm_type == SamplingAlgorithmType.GRID:
+            sampling_algorithm = GridSamplingAlgorithm._from_rest_object(obj)  # pylint: disable=protected-access
+
+        if obj.sampling_algorithm_type == SamplingAlgorithmType.BAYESIAN:
+            sampling_algorithm = BayesianSamplingAlgorithm._from_rest_object(obj)  # pylint: disable=protected-access
+
+        return cast(Optional["SamplingAlgorithm"], sampling_algorithm)
+
+
+class RandomSamplingAlgorithm(SamplingAlgorithm):
+    """Random Sampling Algorithm.
+
+    :keyword rule: The specific type of random algorithm. Accepted values are: "random" and "sobol".
+    :type rule: str
+    :keyword seed: The seed for random number generation.
+    :paramtype seed: int
+    :keyword logbase: A positive number or the number "e" in string format to be used as the base for log
+        based random sampling.
+    :paramtype logbase: Union[float, str]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_random_sampling_algorithm]
+            :end-before: [END configure_sweep_job_random_sampling_algorithm]
+            :language: python
+            :dedent: 8
+            :caption: Assigning a random sampling algorithm for a SweepJob
+    """
+
+    def __init__(
+        self,
+        *,
+        rule: Optional[str] = None,
+        seed: Optional[int] = None,
+        logbase: Optional[Union[float, str]] = None,
+    ) -> None:
+        super().__init__()
+        self.type = SamplingAlgorithmType.RANDOM.lower()
+        self.rule = rule
+        self.seed = seed
+        self.logbase = logbase
+
+    def _to_rest_object(self) -> RestRandomSamplingAlgorithm:
+        return RestRandomSamplingAlgorithm(
+            rule=self.rule,
+            seed=self.seed,
+            logbase=self.logbase,
+        )
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestRandomSamplingAlgorithm) -> "RandomSamplingAlgorithm":
+        return cls(
+            rule=obj.rule,
+            seed=obj.seed,
+            logbase=obj.logbase,
+        )
+
+
+class GridSamplingAlgorithm(SamplingAlgorithm):
+    """Grid Sampling Algorithm.
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_grid_sampling_algorithm]
+            :end-before: [END configure_sweep_job_grid_sampling_algorithm]
+            :language: python
+            :dedent: 8
+            :caption: Assigning a grid sampling algorithm for a SweepJob
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.type = SamplingAlgorithmType.GRID.lower()
+
+    def _to_rest_object(self) -> RestGridSamplingAlgorithm:
+        return RestGridSamplingAlgorithm()
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestGridSamplingAlgorithm) -> "GridSamplingAlgorithm":
+        return cls()
+
+
+class BayesianSamplingAlgorithm(SamplingAlgorithm):
+    """Bayesian Sampling Algorithm.
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_bayesian_sampling_algorithm]
+            :end-before: [END configure_sweep_job_bayesian_sampling_algorithm]
+            :language: python
+            :dedent: 8
+            :caption: Assigning a Bayesian sampling algorithm for a SweepJob
+    """
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.type = SamplingAlgorithmType.BAYESIAN.lower()
+
+    def _to_rest_object(self) -> RestBayesianSamplingAlgorithm:
+        return RestBayesianSamplingAlgorithm()
+
+    @classmethod
+    def _from_rest_object(cls, obj: RestBayesianSamplingAlgorithm) -> "BayesianSamplingAlgorithm":
+        return cls()
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/search_space.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/search_space.py
new file mode 100644
index 00000000..bbc08d98
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/search_space.py
@@ -0,0 +1,393 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from abc import ABC
+from typing import Any, List, Optional, Union
+
+from azure.ai.ml.constants._common import TYPE
+from azure.ai.ml.constants._job.sweep import SearchSpace
+from azure.ai.ml.entities._mixins import RestTranslatableMixin
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, JobException
+
+
+class SweepDistribution(ABC, RestTranslatableMixin):
+    """Base class for sweep distribution configuration.
+
+    This class should not be instantiated directly. Instead, use one of its subclasses.
+
+    :keyword type: Type of distribution.
+    :paramtype type: str
+    """
+
+    def __init__(self, *, type: Optional[str] = None) -> None:  # pylint: disable=redefined-builtin
+        self.type = type
+
+    @classmethod
+    def _from_rest_object(cls, obj: List) -> "SweepDistribution":
+        mapping = {
+            SearchSpace.CHOICE: Choice,
+            SearchSpace.NORMAL: Normal,
+            SearchSpace.LOGNORMAL: LogNormal,
+            SearchSpace.QNORMAL: QNormal,
+            SearchSpace.QLOGNORMAL: QLogNormal,
+            SearchSpace.RANDINT: Randint,
+            SearchSpace.UNIFORM: Uniform,
+            SearchSpace.QUNIFORM: QUniform,
+            SearchSpace.LOGUNIFORM: LogUniform,
+            SearchSpace.QLOGUNIFORM: QLogUniform,
+        }
+
+        ss_class: Any = mapping.get(obj[0], None)
+        if ss_class:
+            res: SweepDistribution = ss_class._from_rest_object(obj)
+            return res
+
+        msg = f"Unknown search space type: {obj[0]}"
+        raise JobException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.SWEEP_JOB,
+            error_category=ErrorCategory.SYSTEM_ERROR,
+        )
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, SweepDistribution):
+            return NotImplemented
+        res: bool = self._to_rest_object() == other._to_rest_object()
+        return res
+
+
+class Choice(SweepDistribution):
+    """Choice distribution configuration.
+
+    :param values: List of values to choose from.
+    :type values: list[Union[float, str, dict]]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_choice_loguniform]
+            :end-before: [END configure_sweep_job_choice_loguniform]
+            :language: python
+            :dedent: 8
+            :caption: Using Choice distribution to set values for a hyperparameter sweep
+    """
+
+    def __init__(self, values: Optional[List[Union[float, str, dict]]] = None, **kwargs: Any) -> None:
+        kwargs.setdefault(TYPE, SearchSpace.CHOICE)
+        super().__init__(**kwargs)
+        self.values = values
+
+    def _to_rest_object(self) -> List:
+        items: List = []
+        if self.values is not None:
+            for value in self.values:
+                if isinstance(value, dict):
+                    rest_dict = {}
+                    for k, v in value.items():
+                        if isinstance(v, SweepDistribution):
+                            rest_dict[k] = v._to_rest_object()
+                        else:
+                            rest_dict[k] = v
+                    items.append(rest_dict)
+                else:
+                    items.append(value)
+        return [self.type, [items]]
+
+    @classmethod
+    def _from_rest_object(cls, obj: List) -> "Choice":
+        rest_values = obj[1][0]
+        from_rest_values = []
+        for rest_value in rest_values:
+            if isinstance(rest_value, dict):
+                from_rest_dict = {}
+                for k, v in rest_value.items():
+                    try:
+                        # first assume that any dictionary value is a valid distribution (i.e. normal, uniform, etc)
+                        # and try to deserialize it into a the correct SDK distribution object
+                        from_rest_dict[k] = SweepDistribution._from_rest_object(v)
+                    except Exception:  # pylint: disable=W0718
+                        # if an exception is raised, assume that the value was not a valid distribution and use the
+                        # value as it is for deserialization
+                        from_rest_dict[k] = v
+                from_rest_values.append(from_rest_dict)
+            else:
+                from_rest_values.append(rest_value)
+        return Choice(values=from_rest_values)  # type: ignore[arg-type]
+
+
+class Normal(SweepDistribution):
+    """Normal distribution configuration.
+
+    :param mu: Mean of the distribution.
+    :type mu: float
+    :param sigma: Standard deviation of the distribution.
+    :type sigma: float
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_randint_normal]
+            :end-before: [END configure_sweep_job_randint_normal]
+            :language: python
+            :dedent: 8
+            :caption: Configuring Normal distributions for a hyperparameter sweep on a Command job.
+    """
+
+    def __init__(self, mu: Optional[float] = None, sigma: Optional[float] = None, **kwargs: Any) -> None:
+        kwargs.setdefault(TYPE, SearchSpace.NORMAL)
+        super().__init__(**kwargs)
+        self.mu = mu
+        self.sigma = sigma
+
+    def _to_rest_object(self) -> List:
+        return [self.type, [self.mu, self.sigma]]
+
+    @classmethod
+    def _from_rest_object(cls, obj: List) -> "Normal":
+        return cls(mu=obj[1][0], sigma=obj[1][1])
+
+
+class LogNormal(Normal):
+    """LogNormal distribution configuration.
+
+    :param mu: Mean of the log of the distribution.
+    :type mu: float
+    :param sigma: Standard deviation of the log of the distribution.
+    :type sigma: float
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_lognormal_qlognormal]
+            :end-before: [END configure_sweep_job_lognormal_qlognormal]
+            :language: python
+            :dedent: 8
+            :caption: Configuring LogNormal distributions for a hyperparameter sweep on a Command job.
+    """
+
+    def __init__(self, mu: Optional[float] = None, sigma: Optional[float] = None, **kwargs: Any) -> None:
+        kwargs.setdefault(TYPE, SearchSpace.LOGNORMAL)
+        super().__init__(mu=mu, sigma=sigma, **kwargs)
+
+
+class QNormal(Normal):
+    """QNormal distribution configuration.
+
+    :param mu: Mean of the distribution.
+    :type mu: float
+    :param sigma: Standard deviation of the distribution.
+    :type sigma: float
+    :param q: Quantization factor.
+    :type q: int
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_qloguniform_qnormal]
+            :end-before: [END configure_sweep_job_qloguniform_qnormal]
+            :language: python
+            :dedent: 8
+            :caption: Configuring QNormal distributions for a hyperparameter sweep on a Command job.
+    """
+
+    def __init__(
+        self, mu: Optional[float] = None, sigma: Optional[float] = None, q: Optional[int] = None, **kwargs: Any
+    ) -> None:
+        kwargs.setdefault(TYPE, SearchSpace.QNORMAL)
+        super().__init__(mu=mu, sigma=sigma, **kwargs)
+        self.q = q
+
+    def _to_rest_object(self) -> List:
+        return [self.type, [self.mu, self.sigma, self.q]]
+
+    @classmethod
+    def _from_rest_object(cls, obj: List) -> "QNormal":
+        return cls(mu=obj[1][0], sigma=obj[1][1], q=obj[1][2])
+
+
+class QLogNormal(QNormal):
+    """QLogNormal distribution configuration.
+
+    :param mu: Mean of the log of the distribution.
+    :type mu: Optional[float]
+    :param sigma: Standard deviation of the log of the distribution.
+    :type sigma: Optional[float]
+    :param q: Quantization factor.
+    :type q: Optional[int]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_lognormal_qlognormal]
+            :end-before: [END configure_sweep_job_lognormal_qlognormal]
+            :language: python
+            :dedent: 8
+            :caption: Configuring QLogNormal distributions for a hyperparameter sweep on a Command job.
+    """
+
+    def __init__(
+        self, mu: Optional[float] = None, sigma: Optional[float] = None, q: Optional[int] = None, **kwargs: Any
+    ) -> None:
+        kwargs.setdefault(TYPE, SearchSpace.QLOGNORMAL)
+        super().__init__(mu=mu, sigma=sigma, q=q, **kwargs)
+
+
+class Randint(SweepDistribution):
+    """Randint distribution configuration.
+
+    :param upper: Upper bound of the distribution.
+    :type upper: int
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_randint_normal]
+            :end-before: [END configure_sweep_job_randint_normal]
+            :language: python
+            :dedent: 8
+            :caption: Configuring Randint distributions for a hyperparameter sweep on a Command job.
+    """
+
+    def __init__(self, upper: Optional[int] = None, **kwargs: Any) -> None:
+        kwargs.setdefault(TYPE, SearchSpace.RANDINT)
+        super().__init__(**kwargs)
+        self.upper = upper
+
+    def _to_rest_object(self) -> List:
+        return [self.type, [self.upper]]
+
+    @classmethod
+    def _from_rest_object(cls, obj: List) -> "Randint":
+        return cls(upper=obj[1][0])
+
+
+class Uniform(SweepDistribution):
+    """
+
+    Uniform distribution configuration.
+
+    :param min_value: Minimum value of the distribution.
+    :type min_value: float
+    :param max_value: Maximum value of the distribution.
+    :type max_value: float
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_uniform]
+            :end-before: [END configure_sweep_job_uniform]
+            :language: python
+            :dedent: 8
+            :caption: Configuring Uniform distributions for learning rates and momentum
+                during a hyperparameter sweep on a Command job.
+    """
+
+    def __init__(self, min_value: Optional[float] = None, max_value: Optional[float] = None, **kwargs: Any) -> None:
+        kwargs.setdefault(TYPE, SearchSpace.UNIFORM)
+        super().__init__(**kwargs)
+        self.min_value = min_value
+        self.max_value = max_value
+
+    def _to_rest_object(self) -> List:
+        return [self.type, [self.min_value, self.max_value]]
+
+    @classmethod
+    def _from_rest_object(cls, obj: List) -> "Uniform":
+        return cls(min_value=obj[1][0], max_value=obj[1][1])
+
+
+class LogUniform(Uniform):
+    """LogUniform distribution configuration.
+
+    :param min_value: Minimum value of the log of the distribution.
+    :type min_value: float
+    :param max_value: Maximum value of the log of the distribution.
+    :type max_value: float
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_choice_loguniform]
+            :end-before: [END configure_sweep_job_choice_loguniform]
+            :language: python
+            :dedent: 8
+            :caption: Configuring a LogUniform distribution for a hyperparameter sweep job learning rate
+    """
+
+    def __init__(self, min_value: Optional[float] = None, max_value: Optional[float] = None, **kwargs: Any) -> None:
+        kwargs.setdefault(TYPE, SearchSpace.LOGUNIFORM)
+        super().__init__(min_value=min_value, max_value=max_value, **kwargs)
+
+
+class QUniform(Uniform):
+    """QUniform distribution configuration.
+
+    :param min_value: Minimum value of the distribution.
+    :type min_value: Optional[Union[int, float]]
+    :param max_value: Maximum value of the distribution.
+    :type max_value: Optional[Union[int, float]]
+    :param q: Quantization factor.
+    :type q: Optional[int]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_truncation_selection_policy]
+            :end-before: [END configure_sweep_job_truncation_selection_policy]
+            :language: python
+            :dedent: 8
+            :caption: Configuring QUniform distributions for a hyperparameter sweep on a Command job.
+    """
+
+    def __init__(
+        self,
+        min_value: Optional[Union[int, float]] = None,
+        max_value: Optional[Union[int, float]] = None,
+        q: Optional[int] = None,
+        **kwargs: Any,
+    ) -> None:
+        kwargs.setdefault(TYPE, SearchSpace.QUNIFORM)
+        super().__init__(min_value=min_value, max_value=max_value, **kwargs)
+        self.q = q
+
+    def _to_rest_object(self) -> List:
+        return [self.type, [self.min_value, self.max_value, self.q]]
+
+    @classmethod
+    def _from_rest_object(cls, obj: List) -> "QUniform":
+        return cls(min_value=obj[1][0], max_value=obj[1][1], q=obj[1][2])
+
+
+class QLogUniform(QUniform):
+    """QLogUniform distribution configuration.
+
+    :param min_value: Minimum value of the log of the distribution.
+    :type min_value: Optional[float]
+    :param max_value: Maximum value of the log of the distribution.
+    :type max_value: Optional[float]
+    :param q: Quantization factor.
+    :type q: Optional[int]
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_qloguniform_qnormal]
+            :end-before: [END configure_sweep_job_qloguniform_qnormal]
+            :language: python
+            :dedent: 8
+            :caption: Configuring QLogUniform distributions for a hyperparameter sweep on a Command job.
+    """
+
+    def __init__(
+        self,
+        min_value: Optional[float] = None,
+        max_value: Optional[float] = None,
+        q: Optional[int] = None,
+        **kwargs: Any,
+    ) -> None:
+        kwargs.setdefault(TYPE, SearchSpace.QLOGUNIFORM)
+        super().__init__(min_value=min_value, max_value=max_value, q=q, **kwargs)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/sweep_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/sweep_job.py
new file mode 100644
index 00000000..0a99bb39
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/sweep/sweep_job.py
@@ -0,0 +1,361 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+import logging
+from typing import Any, Dict, NoReturn, Optional, Union
+
+from azure.ai.ml._restclient.v2023_08_01_preview.models import JobBase
+from azure.ai.ml._restclient.v2023_08_01_preview.models import SweepJob as RestSweepJob
+from azure.ai.ml._restclient.v2023_08_01_preview.models import TrialComponent
+from azure.ai.ml._schema._sweep.sweep_job import SweepJobSchema
+from azure.ai.ml._utils.utils import map_single_brackets_and_warn
+from azure.ai.ml.constants import JobType
+from azure.ai.ml.constants._common import BASE_PATH_CONTEXT_KEY, TYPE
+from azure.ai.ml.entities._component.command_component import CommandComponent
+from azure.ai.ml.entities._credentials import (
+    AmlTokenConfiguration,
+    ManagedIdentityConfiguration,
+    UserIdentityConfiguration,
+    _BaseJobIdentityConfiguration,
+)
+from azure.ai.ml.entities._inputs_outputs import Input
+from azure.ai.ml.entities._job._input_output_helpers import (
+    from_rest_data_outputs,
+    from_rest_inputs_to_dataset_literal,
+    to_rest_data_outputs,
+    to_rest_dataset_literal_inputs,
+    validate_inputs_for_command,
+    validate_key_contains_allowed_characters,
+)
+from azure.ai.ml.entities._job.command_job import CommandJob
+from azure.ai.ml.entities._job.job import Job
+from azure.ai.ml.entities._job.job_io_mixin import JobIOMixin
+from azure.ai.ml.entities._job.job_resource_configuration import JobResourceConfiguration
+from azure.ai.ml.entities._job.sweep.sampling_algorithm import SamplingAlgorithm
+from azure.ai.ml.entities._system_data import SystemData
+from azure.ai.ml.entities._util import load_from_dict
+from azure.ai.ml.exceptions import ErrorCategory, ErrorTarget, JobException
+
+# from ..identity import AmlToken, Identity, ManagedIdentity, UserIdentity
+from ..job_limits import SweepJobLimits
+from ..parameterized_command import ParameterizedCommand
+from ..queue_settings import QueueSettings
+from .early_termination_policy import (
+    BanditPolicy,
+    EarlyTerminationPolicy,
+    MedianStoppingPolicy,
+    TruncationSelectionPolicy,
+)
+from .objective import Objective
+from .parameterized_sweep import ParameterizedSweep
+from .search_space import (
+    Choice,
+    LogNormal,
+    LogUniform,
+    Normal,
+    QLogNormal,
+    QLogUniform,
+    QNormal,
+    QUniform,
+    Randint,
+    SweepDistribution,
+    Uniform,
+)
+
+module_logger = logging.getLogger(__name__)
+
+
+class SweepJob(Job, ParameterizedSweep, JobIOMixin):
+    """Sweep job for hyperparameter tuning.
+
+    .. note::
+        For sweep jobs, inputs, outputs, and parameters are accessible as environment variables using the prefix
+        ``AZUREML_SWEEP_``. For example, if you have a parameter named "learning_rate", you can access it as
+        ``AZUREML_SWEEP_learning_rate``.
+
+    :keyword name: Name of the job.
+    :paramtype name: str
+    :keyword display_name: Display name of the job.
+    :paramtype display_name: str
+    :keyword description: Description of the job.
+    :paramtype description: str
+    :keyword tags: Tag dictionary. Tags can be added, removed, and updated.
+    :paramtype tags: dict[str, str]
+    :keyword properties: The asset property dictionary.
+    :paramtype properties: dict[str, str]
+    :keyword experiment_name:  Name of the experiment the job will be created under. If None is provided,
+        job will be created under experiment 'Default'.
+    :paramtype experiment_name: str
+    :keyword identity: Identity that the training job will use while running on compute.
+    :paramtype identity: Union[
+        ~azure.ai.ml.ManagedIdentityConfiguration,
+        ~azure.ai.ml.AmlTokenConfiguration,
+        ~azure.ai.ml.UserIdentityConfiguration
+
+    ]
+
+    :keyword inputs: Inputs to the command.
+    :paramtype inputs: dict
+    :keyword outputs: Mapping of output data bindings used in the job.
+    :paramtype outputs: dict[str, ~azure.ai.ml.Output]
+    :keyword sampling_algorithm: The hyperparameter sampling algorithm to use over the `search_space`. Defaults to
+        "random".
+
+    :paramtype sampling_algorithm: str
+    :keyword search_space: Dictionary of the hyperparameter search space. The key is the name of the hyperparameter
+        and the value is the parameter expression.
+
+    :paramtype search_space: Dict
+    :keyword objective: Metric to optimize for.
+    :paramtype objective: Objective
+    :keyword compute: The compute target the job runs on.
+    :paramtype compute: str
+    :keyword trial: The job configuration for each trial. Each trial will be provided with a different combination
+        of hyperparameter values that the system samples from the search_space.
+
+    :paramtype trial: Union[
+        ~azure.ai.ml.entities.CommandJob,
+        ~azure.ai.ml.entities.CommandComponent
+
+    ]
+
+    :keyword early_termination: The early termination policy to use. A trial job is canceled
+        when the criteria of the specified policy are met. If omitted, no early termination policy will be applied.
+
+    :paramtype early_termination:  Union[
+        ~azure.mgmt.machinelearningservices.models.BanditPolicy,
+        ~azure.mgmt.machinelearningservices.models.MedianStoppingPolicy,
+        ~azure.mgmt.machinelearningservices.models.TruncationSelectionPolicy
+
+    ]
+
+    :keyword limits: Limits for the sweep job.
+    :paramtype limits: ~azure.ai.ml.entities.SweepJobLimits
+    :keyword queue_settings: Queue settings for the job.
+    :paramtype queue_settings: ~azure.ai.ml.entities.QueueSettings
+    :keyword resources: Compute Resource configuration for the job.
+    :paramtype resources: Optional[Union[~azure.ai.ml.entities.ResourceConfiguration]
+    :keyword kwargs: A dictionary of additional configuration parameters.
+    :paramtype kwargs: dict
+
+
+    .. admonition:: Example:
+
+        .. literalinclude:: ../samples/ml_samples_sweep_configurations.py
+            :start-after: [START configure_sweep_job_bayesian_sampling_algorithm]
+            :end-before: [END configure_sweep_job_bayesian_sampling_algorithm]
+            :language: python
+            :dedent: 8
+            :caption: Creating a SweepJob
+    """
+
+    def __init__(
+        self,
+        *,
+        name: Optional[str] = None,
+        description: Optional[str] = None,
+        tags: Optional[Dict] = None,
+        display_name: Optional[str] = None,
+        experiment_name: Optional[str] = None,
+        identity: Optional[
+            Union[ManagedIdentityConfiguration, AmlTokenConfiguration, UserIdentityConfiguration]
+        ] = None,
+        inputs: Optional[Dict[str, Union[Input, str, bool, int, float]]] = None,
+        outputs: Optional[Dict] = None,
+        compute: Optional[str] = None,
+        limits: Optional[SweepJobLimits] = None,
+        sampling_algorithm: Optional[Union[str, SamplingAlgorithm]] = None,
+        search_space: Optional[
+            Dict[
+                str,
+                Union[
+                    Choice, LogNormal, LogUniform, Normal, QLogNormal, QLogUniform, QNormal, QUniform, Randint, Uniform
+                ],
+            ]
+        ] = None,
+        objective: Optional[Objective] = None,
+        trial: Optional[Union[CommandJob, CommandComponent]] = None,
+        early_termination: Optional[
+            Union[EarlyTerminationPolicy, BanditPolicy, MedianStoppingPolicy, TruncationSelectionPolicy]
+        ] = None,
+        queue_settings: Optional[QueueSettings] = None,
+        resources: Optional[Union[dict, JobResourceConfiguration]] = None,
+        **kwargs: Any,
+    ) -> None:
+        kwargs[TYPE] = JobType.SWEEP
+
+        Job.__init__(
+            self,
+            name=name,
+            description=description,
+            tags=tags,
+            display_name=display_name,
+            experiment_name=experiment_name,
+            compute=compute,
+            **kwargs,
+        )
+        self.inputs = inputs  # type: ignore[assignment]
+        self.outputs = outputs  # type: ignore[assignment]
+        self.trial = trial
+        self.identity = identity
+
+        ParameterizedSweep.__init__(
+            self,
+            limits=limits,
+            sampling_algorithm=sampling_algorithm,
+            objective=objective,
+            early_termination=early_termination,
+            search_space=search_space,
+            queue_settings=queue_settings,
+            resources=resources,
+        )
+
+    def _to_dict(self) -> Dict:
+        res: dict = SweepJobSchema(context={BASE_PATH_CONTEXT_KEY: "./"}).dump(self)
+        return res
+
+    def _to_rest_object(self) -> JobBase:
+        self._override_missing_properties_from_trial()
+        if self.trial is not None:
+            self.trial.command = map_single_brackets_and_warn(self.trial.command)
+
+        if self.search_space is not None:
+            search_space = {param: space._to_rest_object() for (param, space) in self.search_space.items()}
+
+        if self.trial is not None:
+            validate_inputs_for_command(self.trial.command, self.inputs)
+        for key in search_space.keys():  # pylint: disable=possibly-used-before-assignment
+            validate_key_contains_allowed_characters(key)
+
+        if self.trial is not None:
+            trial_component = TrialComponent(
+                code_id=self.trial.code,
+                distribution=(
+                    self.trial.distribution._to_rest_object()
+                    if self.trial.distribution and not isinstance(self.trial.distribution, Dict)
+                    else None
+                ),
+                environment_id=self.trial.environment,
+                command=self.trial.command,
+                environment_variables=self.trial.environment_variables,
+                resources=(
+                    self.trial.resources._to_rest_object()
+                    if self.trial.resources and not isinstance(self.trial.resources, Dict)
+                    else None
+                ),
+            )
+
+        sweep_job = RestSweepJob(
+            display_name=self.display_name,
+            description=self.description,
+            experiment_name=self.experiment_name,
+            search_space=search_space,
+            sampling_algorithm=self._get_rest_sampling_algorithm() if self.sampling_algorithm else None,
+            limits=self.limits._to_rest_object() if self.limits else None,
+            early_termination=(
+                self.early_termination._to_rest_object()
+                if self.early_termination and not isinstance(self.early_termination, str)
+                else None
+            ),
+            properties=self.properties,
+            compute_id=self.compute,
+            objective=self.objective._to_rest_object() if self.objective else None,
+            trial=trial_component,  # pylint: disable=possibly-used-before-assignment
+            tags=self.tags,
+            inputs=to_rest_dataset_literal_inputs(self.inputs, job_type=self.type),
+            outputs=to_rest_data_outputs(self.outputs),
+            identity=self.identity._to_job_rest_object() if self.identity else None,
+            queue_settings=self.queue_settings._to_rest_object() if self.queue_settings else None,
+            resources=(
+                self.resources._to_rest_object() if self.resources and not isinstance(self.resources, dict) else None
+            ),
+        )
+
+        if not sweep_job.resources and sweep_job.trial.resources:
+            sweep_job.resources = sweep_job.trial.resources
+
+        sweep_job_resource = JobBase(properties=sweep_job)
+        sweep_job_resource.name = self.name
+        return sweep_job_resource
+
+    def _to_component(self, context: Optional[Dict] = None, **kwargs: Any) -> NoReturn:
+        msg = "no sweep component entity"
+        raise JobException(
+            message=msg,
+            no_personal_data_message=msg,
+            target=ErrorTarget.SWEEP_JOB,
+            error_category=ErrorCategory.USER_ERROR,
+        )
+
+    @classmethod
+    def _load_from_dict(cls, data: Dict, context: Dict, additional_message: str, **kwargs: Any) -> "SweepJob":
+        loaded_schema = load_from_dict(SweepJobSchema, data, context, additional_message, **kwargs)
+        loaded_schema["trial"] = ParameterizedCommand(**(loaded_schema["trial"]))
+        sweep_job = SweepJob(base_path=context[BASE_PATH_CONTEXT_KEY], **loaded_schema)
+        return sweep_job
+
+    @classmethod
+    def _load_from_rest(cls, obj: JobBase) -> "SweepJob":
+        properties: RestSweepJob = obj.properties
+
+        # Unpack termination schema
+        early_termination = EarlyTerminationPolicy._from_rest_object(properties.early_termination)
+
+        # Unpack sampling algorithm
+        sampling_algorithm = SamplingAlgorithm._from_rest_object(properties.sampling_algorithm)
+
+        trial = ParameterizedCommand._load_from_sweep_job(obj.properties)
+        # Compute also appears in both layers of the yaml, but only one of the REST.
+        # This should be a required field in one place, but cannot be if its optional in two
+
+        _search_space = {}
+        for param, dist in properties.search_space.items():
+            _search_space[param] = SweepDistribution._from_rest_object(dist)
+
+        return SweepJob(
+            name=obj.name,
+            id=obj.id,
+            display_name=properties.display_name,
+            description=properties.description,
+            properties=properties.properties,
+            tags=properties.tags,
+            experiment_name=properties.experiment_name,
+            services=properties.services,
+            status=properties.status,
+            creation_context=SystemData._from_rest_object(obj.system_data) if obj.system_data else None,
+            trial=trial,  # type: ignore[arg-type]
+            compute=properties.compute_id,
+            sampling_algorithm=sampling_algorithm,
+            search_space=_search_space,  # type: ignore[arg-type]
+            limits=SweepJobLimits._from_rest_object(properties.limits),
+            early_termination=early_termination,
+            objective=Objective._from_rest_object(properties.objective) if properties.objective else None,
+            inputs=from_rest_inputs_to_dataset_literal(properties.inputs),
+            outputs=from_rest_data_outputs(properties.outputs),
+            identity=(
+                _BaseJobIdentityConfiguration._from_rest_object(properties.identity) if properties.identity else None
+            ),
+            queue_settings=properties.queue_settings,
+            resources=properties.resources if hasattr(properties, "resources") else None,
+        )
+
+    def _override_missing_properties_from_trial(self) -> None:
+        if not isinstance(self.trial, CommandJob):
+            return
+
+        if not self.compute:
+            self.compute = self.trial.compute
+        if not self.inputs:
+            self.inputs = self.trial.inputs
+        if not self.outputs:
+            self.outputs = self.trial.outputs
+
+        has_trial_limits_timeout = self.trial.limits and self.trial.limits.timeout
+        if has_trial_limits_timeout and not self.limits:
+            time_out = self.trial.limits.timeout if self.trial.limits is not None else None
+            self.limits = SweepJobLimits(trial_timeout=time_out)
+        elif has_trial_limits_timeout and self.limits is not None and not self.limits.trial_timeout:
+            self.limits.trial_timeout = self.trial.limits.timeout if self.trial.limits is not None else None
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/to_rest_functions.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/to_rest_functions.py
new file mode 100644
index 00000000..472cbc91
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/entities/_job/to_rest_functions.py
@@ -0,0 +1,82 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=protected-access
+
+from functools import singledispatch
+from pathlib import Path
+from typing import Any
+
+from azure.ai.ml._restclient.v2023_08_01_preview.models import JobBase as JobBaseData
+from azure.ai.ml._restclient.v2025_01_01_preview.models import JobBase as JobBaseData202501
+from azure.ai.ml.constants._common import DEFAULT_EXPERIMENT_NAME
+from azure.ai.ml.entities._builders.command import Command
+from azure.ai.ml.entities._builders.pipeline import Pipeline
+from azure.ai.ml.entities._builders.spark import Spark
+from azure.ai.ml.entities._builders.sweep import Sweep
+from azure.ai.ml.entities._job.job_name_generator import generate_job_name
+
+from .import_job import ImportJob
+from .job import Job
+
+
+def generate_defaults(job: Job, rest_job: JobBaseData) -> None:
+    # Default name to a generated user friendly name.
+    if not job.name:
+        rest_job.name = generate_job_name()
+
+    if not job.display_name:
+        rest_job.properties.display_name = rest_job.name
+
+    # Default experiment to current folder name or "Default"
+    if not job.experiment_name:
+        rest_job.properties.experiment_name = Path("./").resolve().stem.replace(" ", "") or DEFAULT_EXPERIMENT_NAME
+
+
+@singledispatch
+def to_rest_job_object(something: Any) -> JobBaseData:
+    raise NotImplementedError()
+
+
+@to_rest_job_object.register(Job)
+def _(job: Job) -> JobBaseData:
+    # TODO: Bug Item number: 2883432
+    rest_job = job._to_rest_object()  # type: ignore
+    generate_defaults(job, rest_job)
+    return rest_job
+
+
+@to_rest_job_object.register(Command)
+def _(command: Command) -> JobBaseData202501:
+    rest_job = command._to_job()._to_rest_object()
+    generate_defaults(command, rest_job)
+    return rest_job
+
+
+@to_rest_job_object.register(Sweep)
+def _(sweep: Sweep) -> JobBaseData:
+    rest_job = sweep._to_job()._to_rest_object()
+    generate_defaults(sweep, rest_job)
+    return rest_job
+
+
+@to_rest_job_object.register(Pipeline)
+def _(pipeline: Pipeline) -> JobBaseData:
+    rest_job = pipeline._to_job()._to_rest_object()
+    generate_defaults(pipeline, rest_job)
+    return rest_job
+
+
+@to_rest_job_object.register(Spark)
+def _(spark: Spark) -> JobBaseData:
+    rest_job = spark._to_job()._to_rest_object()
+    generate_defaults(spark, rest_job)
+    return rest_job
+
+
+@to_rest_job_object.register(ImportJob)
+def _(importJob: ImportJob) -> JobBaseData:
+    rest_job = importJob._to_rest_object()
+    generate_defaults(importJob, rest_job)
+    return rest_job