Package spec (1.95.1)

Data drift monitoring spec.

Data drift measures the distribution distance between the current dataset and a baseline dataset. A typical use case is to detect data drift between the recent production serving dataset and the training dataset, or to compare the recent production dataset with a dataset from a previous period.

.. rubric:: Example

feature_drift_spec=DataDriftSpec( features=["feature1"] categorical_metric_type="l_infinity", numeric_metric_type="jensen_shannon_divergence", default_categorical_alert_threshold=0.01, default_numeric_alert_threshold=0.02, feature_alert_thresholds={"feature1":0.02, "feature2":0.01}, )

Feature attribution spec.

.. rubric:: Example

feature_attribution_spec=FeatureAttributionSpec( features=["feature1"] default_alert_threshold=0.01, feature_alert_thresholds={"feature1":0.02, "feature2":0.01}, batch_dedicated_resources=BatchDedicatedResources( starting_replica_count=1, max_replica_count=2, machine_spec=my_machine_spec, ), )

Field Schema.

The class identifies the data type of a single feature, which combines together to form the Schema for different fields in ModelMonitoringSchema.

Initializer for ModelMonitoringSchema.

Model monitoring data input spec.

Initializer for NotificationSpec.

Initializer for ObjectiveSpec.

Initializer for OutputSpec.

Initializer for TabularObjective.