Class ModelMonitor (1.51.0)

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ModelMonitor(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Attributes
Name	Description
tabular_objective	google.cloud.aiplatform_v1beta1.types.ModelMonitoringObjectiveSpec.TabularObjective Optional default tabular model monitoring objective. This field is a member of oneof_ default_objective.
name	str Immutable. Resource name of the ModelMonitor. Format: projects/{project}/locations/{location}/modelMonitors/{model_monitor}.
display_name	str The display name of the ModelMonitor. The name can be up to 128 characters long and can consist of any UTF-8.
model_monitoring_target	google.cloud.aiplatform_v1beta1.types.ModelMonitor.ModelMonitoringTarget The entity that is subject to analysis. Currently only models in Vertex AI Model Registry are supported. If you want to analyze the model which is outside the Vertex AI, you could register a model in Vertex AI Model Registry using just a display name.
training_dataset	google.cloud.aiplatform_v1beta1.types.ModelMonitoringInput Optional training dataset used to train the model. It can serve as a reference dataset to identify changes in production.
notification_spec	google.cloud.aiplatform_v1beta1.types.ModelMonitoringNotificationSpec Optional default notification spec, it can be overridden in the ModelMonitoringJob notification spec.
output_spec	google.cloud.aiplatform_v1beta1.types.ModelMonitoringOutputSpec Optional default monitoring metrics/logs export spec, it can be overridden in the ModelMonitoringJob output spec. If not specified, a default Google Cloud Storage bucket will be created under your project.
explanation_spec	google.cloud.aiplatform_v1beta1.types.ExplanationSpec Optional model explanation spec. It is used for feature attribution monitoring.
model_monitoring_schema	google.cloud.aiplatform_v1beta1.types.ModelMonitoringSchema Monitoring Schema is to specify the model's features, prediction outputs and ground truth properties. It is used to extract pertinent data from the dataset and to process features based on their properties. Make sure that the schema aligns with your dataset, if it does not, we will be unable to extract data from the dataset. It is required for most models, but optional for Vertex AI AutoML Tables unless the schem information is not available.
create_time	google.protobuf.timestamp_pb2.Timestamp Output only. Timestamp when this ModelMonitor was created.
update_time	google.protobuf.timestamp_pb2.Timestamp Output only. Timestamp when this ModelMonitor was updated most recently.

Classes

ModelMonitoringTarget

ModelMonitoringTarget(mapping=None, *, ignore_unknown_fields=False, **kwargs)

The monitoring target refers to the entity that is subject to analysis. e.g. Vertex AI Model version.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields

Methods

ModelMonitor

ModelMonitor(mapping=None, *, ignore_unknown_fields=False, **kwargs)

Vertex AI Model Monitoring Service serves as a central hub for the analysis and visualization of data quality and performance related to models. ModelMonitor stands as a top level resource for overseeing your model monitoring tasks.

.. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields