two version of R2R are here HEAD master

9 files changed, 747 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/__init__.py
new file mode 100644
index 00000000..29a4fcd3
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/__init__.py
@@ -0,0 +1,5 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)  # type: ignore
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/alert_notification.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/alert_notification.py
new file mode 100644
index 00000000..bd7fd69c
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/alert_notification.py
@@ -0,0 +1,19 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=unused-argument
+
+from marshmallow import fields, post_load
+
+from azure.ai.ml._schema.core.schema import PatchedSchemaMeta
+
+
+class AlertNotificationSchema(metaclass=PatchedSchemaMeta):
+    emails = fields.List(fields.Str)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.alert_notification import AlertNotification
+
+        return AlertNotification(**data)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/compute.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/compute.py
new file mode 100644
index 00000000..483b4ac5
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/compute.py
@@ -0,0 +1,23 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=unused-argument
+
+from marshmallow import fields, post_load
+from azure.ai.ml._schema.core.schema import PatchedSchemaMeta
+
+
+class ComputeConfigurationSchema(metaclass=PatchedSchemaMeta):
+    compute_type = fields.Str(allowed_values=["ServerlessSpark"])
+
+
+class ServerlessSparkComputeSchema(ComputeConfigurationSchema):
+    runtime_version = fields.Str()
+    instance_type = fields.Str()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.compute import ServerlessSparkCompute
+
+        return ServerlessSparkCompute(**data)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/input_data.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/input_data.py
new file mode 100644
index 00000000..d5a6a4f9
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/input_data.py
@@ -0,0 +1,52 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=unused-argument
+
+from marshmallow import fields, post_load
+
+from azure.ai.ml.constants._monitoring import MonitorDatasetContext
+from azure.ai.ml._schema.core.schema import PatchedSchemaMeta
+from azure.ai.ml._schema.core.fields import NestedField, StringTransformedEnum, UnionField
+from azure.ai.ml._schema.job.input_output_entry import MLTableInputSchema, DataInputSchema
+
+
+class MonitorInputDataSchema(metaclass=PatchedSchemaMeta):
+    input_data = UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    data_context = StringTransformedEnum(allowed_values=[o.value for o in MonitorDatasetContext])
+    target_columns = fields.Dict()
+    job_type = fields.Str()
+    uri = fields.Str()
+
+
+class FixedInputDataSchema(MonitorInputDataSchema):
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.input_data import FixedInputData
+
+        return FixedInputData(**data)
+
+
+class TrailingInputDataSchema(MonitorInputDataSchema):
+    window_size = fields.Str()
+    window_offset = fields.Str()
+    pre_processing_component_id = fields.Str()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.input_data import TrailingInputData
+
+        return TrailingInputData(**data)
+
+
+class StaticInputDataSchema(MonitorInputDataSchema):
+    pre_processing_component_id = fields.Str()
+    window_start = fields.String()
+    window_end = fields.String()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.input_data import StaticInputData
+
+        return StaticInputData(**data)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/monitor_definition.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/monitor_definition.py
new file mode 100644
index 00000000..3fe52c9d
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/monitor_definition.py
@@ -0,0 +1,53 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=unused-argument
+
+from marshmallow import fields, post_load
+
+from azure.ai.ml.constants._monitoring import AZMONITORING
+from azure.ai.ml._schema.monitoring.target import MonitoringTargetSchema
+from azure.ai.ml._schema.monitoring.compute import ServerlessSparkComputeSchema
+from azure.ai.ml._schema.monitoring.signals import (
+    DataDriftSignalSchema,
+    DataQualitySignalSchema,
+    PredictionDriftSignalSchema,
+    FeatureAttributionDriftSignalSchema,
+    CustomMonitoringSignalSchema,
+    GenerationSafetyQualitySchema,
+    ModelPerformanceSignalSchema,
+    GenerationTokenStatisticsSchema,
+)
+from azure.ai.ml._schema.monitoring.alert_notification import AlertNotificationSchema
+from azure.ai.ml._schema.core.fields import NestedField, UnionField, StringTransformedEnum
+from azure.ai.ml._schema.core.schema import PatchedSchemaMeta
+
+
+class MonitorDefinitionSchema(metaclass=PatchedSchemaMeta):
+    compute = NestedField(ServerlessSparkComputeSchema)
+    monitoring_target = NestedField(MonitoringTargetSchema)
+    monitoring_signals = fields.Dict(
+        keys=fields.Str(),
+        values=UnionField(
+            union_fields=[
+                NestedField(DataDriftSignalSchema),
+                NestedField(DataQualitySignalSchema),
+                NestedField(PredictionDriftSignalSchema),
+                NestedField(FeatureAttributionDriftSignalSchema),
+                NestedField(CustomMonitoringSignalSchema),
+                NestedField(GenerationSafetyQualitySchema),
+                NestedField(ModelPerformanceSignalSchema),
+                NestedField(GenerationTokenStatisticsSchema),
+            ]
+        ),
+    )
+    alert_notification = UnionField(
+        union_fields=[StringTransformedEnum(allowed_values=AZMONITORING), NestedField(AlertNotificationSchema)]
+    )
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.definition import MonitorDefinition
+
+        return MonitorDefinition(**data)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/schedule.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/schedule.py
new file mode 100644
index 00000000..a2034d33
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/schedule.py
@@ -0,0 +1,11 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from azure.ai.ml._schema.core.fields import NestedField
+from azure.ai.ml._schema.monitoring.monitor_definition import MonitorDefinitionSchema
+from azure.ai.ml._schema.schedule.schedule import ScheduleSchema
+
+
+class MonitorScheduleSchema(ScheduleSchema):
+    create_monitor = NestedField(MonitorDefinitionSchema)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/signals.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/signals.py
new file mode 100644
index 00000000..4f55393b
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/signals.py
@@ -0,0 +1,348 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=unused-argument
+
+from marshmallow import fields, post_load, pre_dump, ValidationError
+
+from azure.ai.ml._schema.job.input_output_entry import DataInputSchema, MLTableInputSchema
+from azure.ai.ml.constants._common import AzureMLResourceType
+from azure.ai.ml.constants._monitoring import (
+    MonitorSignalType,
+    ALL_FEATURES,
+    MonitorModelType,
+    MonitorDatasetContext,
+    FADColumnNames,
+)
+from azure.ai.ml._schema.core.schema import PatchedSchemaMeta
+from azure.ai.ml._schema.core.fields import ArmVersionedStr, NestedField, UnionField, StringTransformedEnum
+from azure.ai.ml._schema.monitoring.thresholds import (
+    DataDriftMetricThresholdSchema,
+    DataQualityMetricThresholdSchema,
+    PredictionDriftMetricThresholdSchema,
+    FeatureAttributionDriftMetricThresholdSchema,
+    ModelPerformanceMetricThresholdSchema,
+    CustomMonitoringMetricThresholdSchema,
+    GenerationSafetyQualityMetricThresholdSchema,
+    GenerationTokenStatisticsMonitorMetricThresholdSchema,
+)
+
+
+class DataSegmentSchema(metaclass=PatchedSchemaMeta):
+    feature_name = fields.Str()
+    feature_values = fields.List(fields.Str)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import DataSegment
+
+        return DataSegment(**data)
+
+
+class MonitorFeatureFilterSchema(metaclass=PatchedSchemaMeta):
+    top_n_feature_importance = fields.Int()
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import MonitorFeatureFilter
+
+        if not isinstance(data, MonitorFeatureFilter):
+            raise ValidationError("Cannot dump non-MonitorFeatureFilter object into MonitorFeatureFilter")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import MonitorFeatureFilter
+
+        return MonitorFeatureFilter(**data)
+
+
+class BaselineDataRangeSchema(metaclass=PatchedSchemaMeta):
+    window_start = fields.Str()
+    window_end = fields.Str()
+    lookback_window_size = fields.Str()
+    lookback_window_offset = fields.Str()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import BaselineDataRange
+
+        return BaselineDataRange(**data)
+
+
+class ProductionDataSchema(metaclass=PatchedSchemaMeta):
+    input_data = UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    data_context = StringTransformedEnum(allowed_values=[o.value for o in MonitorDatasetContext])
+    pre_processing_component = fields.Str()
+    data_window = NestedField(BaselineDataRangeSchema)
+    data_column_names = fields.Dict(keys=fields.Str(), values=fields.Str())
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import ProductionData
+
+        return ProductionData(**data)
+
+
+class ReferenceDataSchema(metaclass=PatchedSchemaMeta):
+    input_data = UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    data_context = StringTransformedEnum(allowed_values=[o.value for o in MonitorDatasetContext])
+    pre_processing_component = fields.Str()
+    target_column_name = fields.Str()
+    data_window = NestedField(BaselineDataRangeSchema)
+    data_column_names = fields.Dict(keys=fields.Str(), values=fields.Str())
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import ReferenceData
+
+        return ReferenceData(**data)
+
+
+class MonitoringSignalSchema(metaclass=PatchedSchemaMeta):
+    production_data = NestedField(ProductionDataSchema)
+    reference_data = NestedField(ReferenceDataSchema)
+    properties = fields.Dict()
+    alert_enabled = fields.Bool()
+
+
+class DataSignalSchema(MonitoringSignalSchema):
+    features = UnionField(
+        union_fields=[
+            NestedField(MonitorFeatureFilterSchema),
+            StringTransformedEnum(allowed_values=ALL_FEATURES),
+            fields.List(fields.Str),
+        ]
+    )
+    feature_type_override = fields.Dict()
+
+
+class DataDriftSignalSchema(DataSignalSchema):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.DATA_DRIFT, required=True)
+    metric_thresholds = NestedField(DataDriftMetricThresholdSchema)
+    data_segment = NestedField(DataSegmentSchema)
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import DataDriftSignal
+
+        if not isinstance(data, DataDriftSignal):
+            raise ValidationError("Cannot dump non-DataDriftSignal object into DataDriftSignal")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import DataDriftSignal
+
+        data.pop("type", None)
+        return DataDriftSignal(**data)
+
+
+class DataQualitySignalSchema(DataSignalSchema):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.DATA_QUALITY, required=True)
+    metric_thresholds = NestedField(DataQualityMetricThresholdSchema)
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import DataQualitySignal
+
+        if not isinstance(data, DataQualitySignal):
+            raise ValidationError("Cannot dump non-DataQualitySignal object into DataQualitySignal")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import DataQualitySignal
+
+        data.pop("type", None)
+        return DataQualitySignal(**data)
+
+
+class PredictionDriftSignalSchema(MonitoringSignalSchema):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.PREDICTION_DRIFT, required=True)
+    metric_thresholds = NestedField(PredictionDriftMetricThresholdSchema)
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import PredictionDriftSignal
+
+        if not isinstance(data, PredictionDriftSignal):
+            raise ValidationError("Cannot dump non-PredictionDriftSignal object into PredictionDriftSignal")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import PredictionDriftSignal
+
+        data.pop("type", None)
+        return PredictionDriftSignal(**data)
+
+
+class ModelSignalSchema(MonitoringSignalSchema):
+    model_type = StringTransformedEnum(allowed_values=[MonitorModelType.CLASSIFICATION, MonitorModelType.REGRESSION])
+
+
+class FADProductionDataSchema(metaclass=PatchedSchemaMeta):
+    input_data = UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    data_context = StringTransformedEnum(allowed_values=[o.value for o in MonitorDatasetContext])
+    data_column_names = fields.Dict(
+        keys=StringTransformedEnum(allowed_values=[o.value for o in FADColumnNames]), values=fields.Str()
+    )
+    pre_processing_component = fields.Str()
+    data_window = NestedField(BaselineDataRangeSchema)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import FADProductionData
+
+        return FADProductionData(**data)
+
+
+class FeatureAttributionDriftSignalSchema(metaclass=PatchedSchemaMeta):
+    production_data = fields.List(NestedField(FADProductionDataSchema))
+    reference_data = NestedField(ReferenceDataSchema)
+    alert_enabled = fields.Bool()
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.FEATURE_ATTRIBUTION_DRIFT, required=True)
+    metric_thresholds = NestedField(FeatureAttributionDriftMetricThresholdSchema)
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import FeatureAttributionDriftSignal
+
+        if not isinstance(data, FeatureAttributionDriftSignal):
+            raise ValidationError(
+                "Cannot dump non-FeatureAttributionDriftSignal object into FeatureAttributionDriftSignal"
+            )
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import FeatureAttributionDriftSignal
+
+        data.pop("type", None)
+        return FeatureAttributionDriftSignal(**data)
+
+
+class ModelPerformanceSignalSchema(metaclass=PatchedSchemaMeta):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.MODEL_PERFORMANCE, required=True)
+    production_data = NestedField(ProductionDataSchema)
+    reference_data = NestedField(ReferenceDataSchema)
+    data_segment = NestedField(DataSegmentSchema)
+    alert_enabled = fields.Bool()
+    metric_thresholds = NestedField(ModelPerformanceMetricThresholdSchema)
+    properties = fields.Dict()
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import ModelPerformanceSignal
+
+        if not isinstance(data, ModelPerformanceSignal):
+            raise ValidationError("Cannot dump non-ModelPerformanceSignal object into ModelPerformanceSignal")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import ModelPerformanceSignal
+
+        data.pop("type", None)
+        return ModelPerformanceSignal(**data)
+
+
+class ConnectionSchema(metaclass=PatchedSchemaMeta):
+    environment_variables = fields.Dict(keys=fields.Str(), values=fields.Str())
+    secret_config = fields.Dict(keys=fields.Str(), values=fields.Str())
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import Connection
+
+        return Connection(**data)
+
+
+class CustomMonitoringSignalSchema(metaclass=PatchedSchemaMeta):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.CUSTOM, required=True)
+    connection = NestedField(ConnectionSchema)
+    component_id = ArmVersionedStr(azureml_type=AzureMLResourceType.COMPONENT)
+    metric_thresholds = fields.List(NestedField(CustomMonitoringMetricThresholdSchema))
+    input_data = fields.Dict(keys=fields.Str(), values=NestedField(ReferenceDataSchema))
+    alert_enabled = fields.Bool()
+    inputs = fields.Dict(
+        keys=fields.Str, values=UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    )
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import CustomMonitoringSignal
+
+        if not isinstance(data, CustomMonitoringSignal):
+            raise ValidationError("Cannot dump non-CustomMonitoringSignal object into CustomMonitoringSignal")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import CustomMonitoringSignal
+
+        data.pop("type", None)
+        return CustomMonitoringSignal(**data)
+
+
+class LlmDataSchema(metaclass=PatchedSchemaMeta):
+    input_data = UnionField(union_fields=[NestedField(DataInputSchema), NestedField(MLTableInputSchema)])
+    data_column_names = fields.Dict()
+    data_window = NestedField(BaselineDataRangeSchema)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import LlmData
+
+        return LlmData(**data)
+
+
+class GenerationSafetyQualitySchema(metaclass=PatchedSchemaMeta):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.GENERATION_SAFETY_QUALITY, required=True)
+    production_data = fields.List(NestedField(LlmDataSchema))
+    connection_id = fields.Str()
+    metric_thresholds = NestedField(GenerationSafetyQualityMetricThresholdSchema)
+    alert_enabled = fields.Bool()
+    properties = fields.Dict()
+    sampling_rate = fields.Float()
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import GenerationSafetyQualitySignal
+
+        if not isinstance(data, GenerationSafetyQualitySignal):
+            raise ValidationError("Cannot dump non-GenerationSafetyQuality object into GenerationSafetyQuality")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import GenerationSafetyQualitySignal
+
+        data.pop("type", None)
+        return GenerationSafetyQualitySignal(**data)
+
+
+class GenerationTokenStatisticsSchema(metaclass=PatchedSchemaMeta):
+    type = StringTransformedEnum(allowed_values=MonitorSignalType.GENERATION_TOKEN_STATISTICS, required=True)
+    production_data = NestedField(LlmDataSchema)
+    metric_thresholds = NestedField(GenerationTokenStatisticsMonitorMetricThresholdSchema)
+    alert_enabled = fields.Bool()
+    properties = fields.Dict()
+    sampling_rate = fields.Float()
+
+    @pre_dump
+    def predump(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import GenerationTokenStatisticsSignal
+
+        if not isinstance(data, GenerationTokenStatisticsSignal):
+            raise ValidationError("Cannot dump non-GenerationSafetyQuality object into GenerationSafetyQuality")
+        return data
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.signals import GenerationTokenStatisticsSignal
+
+        data.pop("type", None)
+        return GenerationTokenStatisticsSignal(**data)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/target.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/target.py
new file mode 100644
index 00000000..6d3032ca
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/target.py
@@ -0,0 +1,25 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=unused-argument
+
+from marshmallow import fields, post_load
+
+
+from azure.ai.ml.constants._common import AzureMLResourceType
+from azure.ai.ml.constants._monitoring import MonitorTargetTasks
+from azure.ai.ml._schema.core.schema import PatchedSchemaMeta
+from azure.ai.ml._schema.core.fields import ArmVersionedStr, StringTransformedEnum
+
+
+class MonitoringTargetSchema(metaclass=PatchedSchemaMeta):
+    model_id = ArmVersionedStr(azureml_type=AzureMLResourceType.MODEL)
+    ml_task = StringTransformedEnum(allowed_values=[o.value for o in MonitorTargetTasks])
+    endpoint_deployment_id = fields.Str()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.target import MonitoringTarget
+
+        return MonitoringTarget(**data)
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/thresholds.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/thresholds.py
new file mode 100644
index 00000000..b7970fca
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/_schema/monitoring/thresholds.py
@@ -0,0 +1,211 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+# pylint: disable=unused-argument, name-too-long
+
+from marshmallow import fields, post_load
+
+from azure.ai.ml.constants._monitoring import MonitorFeatureType
+from azure.ai.ml._schema.core.fields import StringTransformedEnum, NestedField
+from azure.ai.ml._schema.core.schema import PatchedSchemaMeta
+
+
+class MetricThresholdSchema(metaclass=PatchedSchemaMeta):
+    threshold = fields.Number()
+
+
+class NumericalDriftMetricsSchema(metaclass=PatchedSchemaMeta):
+    jensen_shannon_distance = fields.Number()
+    normalized_wasserstein_distance = fields.Number()
+    population_stability_index = fields.Number()
+    two_sample_kolmogorov_smirnov_test = fields.Number()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import NumericalDriftMetrics
+
+        return NumericalDriftMetrics(**data)
+
+
+class CategoricalDriftMetricsSchema(metaclass=PatchedSchemaMeta):
+    jensen_shannon_distance = fields.Number()
+    population_stability_index = fields.Number()
+    pearsons_chi_squared_test = fields.Number()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import CategoricalDriftMetrics
+
+        return CategoricalDriftMetrics(**data)
+
+
+class DataDriftMetricThresholdSchema(MetricThresholdSchema):
+    data_type = StringTransformedEnum(allowed_values=[MonitorFeatureType.NUMERICAL, MonitorFeatureType.CATEGORICAL])
+
+    numerical = NestedField(NumericalDriftMetricsSchema)
+    categorical = NestedField(CategoricalDriftMetricsSchema)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import DataDriftMetricThreshold
+
+        return DataDriftMetricThreshold(**data)
+
+
+class DataQualityMetricsNumericalSchema(metaclass=PatchedSchemaMeta):
+    null_value_rate = fields.Number()
+    data_type_error_rate = fields.Number()
+    out_of_bounds_rate = fields.Number()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import DataQualityMetricsNumerical
+
+        return DataQualityMetricsNumerical(**data)
+
+
+class DataQualityMetricsCategoricalSchema(metaclass=PatchedSchemaMeta):
+    null_value_rate = fields.Number()
+    data_type_error_rate = fields.Number()
+    out_of_bounds_rate = fields.Number()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import DataQualityMetricsCategorical
+
+        return DataQualityMetricsCategorical(**data)
+
+
+class DataQualityMetricThresholdSchema(MetricThresholdSchema):
+    data_type = StringTransformedEnum(allowed_values=[MonitorFeatureType.NUMERICAL, MonitorFeatureType.CATEGORICAL])
+    numerical = NestedField(DataQualityMetricsNumericalSchema)
+    categorical = NestedField(DataQualityMetricsCategoricalSchema)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import DataQualityMetricThreshold
+
+        return DataQualityMetricThreshold(**data)
+
+
+class PredictionDriftMetricThresholdSchema(MetricThresholdSchema):
+    data_type = StringTransformedEnum(allowed_values=[MonitorFeatureType.NUMERICAL, MonitorFeatureType.CATEGORICAL])
+    numerical = NestedField(NumericalDriftMetricsSchema)
+    categorical = NestedField(CategoricalDriftMetricsSchema)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import PredictionDriftMetricThreshold
+
+        return PredictionDriftMetricThreshold(**data)
+
+
+# pylint: disable-next=name-too-long
+class FeatureAttributionDriftMetricThresholdSchema(MetricThresholdSchema):
+    normalized_discounted_cumulative_gain = fields.Number()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import FeatureAttributionDriftMetricThreshold
+
+        return FeatureAttributionDriftMetricThreshold(**data)
+
+
+class ModelPerformanceClassificationThresholdsSchema(metaclass=PatchedSchemaMeta):
+    accuracy = fields.Number()
+    precision = fields.Number()
+    recall = fields.Number()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import ModelPerformanceClassificationThresholds
+
+        return ModelPerformanceClassificationThresholds(**data)
+
+
+class ModelPerformanceRegressionThresholdsSchema(metaclass=PatchedSchemaMeta):
+    mae = fields.Number()
+    mse = fields.Number()
+    rmse = fields.Number()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import ModelPerformanceRegressionThresholds
+
+        return ModelPerformanceRegressionThresholds(**data)
+
+
+class ModelPerformanceMetricThresholdSchema(MetricThresholdSchema):
+    classification = NestedField(ModelPerformanceClassificationThresholdsSchema)
+    regression = NestedField(ModelPerformanceRegressionThresholdsSchema)
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import ModelPerformanceMetricThreshold
+
+        return ModelPerformanceMetricThreshold(**data)
+
+
+class CustomMonitoringMetricThresholdSchema(MetricThresholdSchema):
+    metric_name = fields.Str()
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import CustomMonitoringMetricThreshold
+
+        return CustomMonitoringMetricThreshold(**data)
+
+
+class GenerationSafetyQualityMetricThresholdSchema(metaclass=PatchedSchemaMeta):  # pylint: disable=name-too-long
+    groundedness = fields.Dict(
+        keys=StringTransformedEnum(
+            allowed_values=["aggregated_groundedness_pass_rate", "acceptable_groundedness_score_per_instance"]
+        ),
+        values=fields.Number(),
+    )
+    relevance = fields.Dict(
+        keys=StringTransformedEnum(
+            allowed_values=["aggregated_relevance_pass_rate", "acceptable_relevance_score_per_instance"]
+        ),
+        values=fields.Number(),
+    )
+    coherence = fields.Dict(
+        keys=StringTransformedEnum(
+            allowed_values=["aggregated_coherence_pass_rate", "acceptable_coherence_score_per_instance"]
+        ),
+        values=fields.Number(),
+    )
+    fluency = fields.Dict(
+        keys=StringTransformedEnum(
+            allowed_values=["aggregated_fluency_pass_rate", "acceptable_fluency_score_per_instance"]
+        ),
+        values=fields.Number(),
+    )
+    similarity = fields.Dict(
+        keys=StringTransformedEnum(
+            allowed_values=["aggregated_similarity_pass_rate", "acceptable_similarity_score_per_instance"]
+        ),
+        values=fields.Number(),
+    )
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import GenerationSafetyQualityMonitoringMetricThreshold
+
+        return GenerationSafetyQualityMonitoringMetricThreshold(**data)
+
+
+class GenerationTokenStatisticsMonitorMetricThresholdSchema(
+    metaclass=PatchedSchemaMeta
+):  # pylint: disable=name-too-long
+    totaltoken = fields.Dict(
+        keys=StringTransformedEnum(allowed_values=["total_token_count", "total_token_count_per_group"]),
+        values=fields.Number(),
+    )
+
+    @post_load
+    def make(self, data, **kwargs):
+        from azure.ai.ml.entities._monitoring.thresholds import GenerationTokenStatisticsMonitorMetricThreshold
+
+        return GenerationTokenStatisticsMonitorMetricThreshold(**data)