API documentation for spec package.
Classes
DataDriftSpec
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}, )
FeatureAttributionSpec
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, ), )
FieldSchema
Field Schema.
The class identifies the data type of a single feature, which combines together to form the Schema for different fields in ModelMonitoringSchema.
ModelMonitoringSchema
Initializer for ModelMonitoringSchema.
MonitoringInput
Model monitoring data input spec.
NotificationSpec
Initializer for NotificationSpec.
ObjectiveSpec
Initializer for ObjectiveSpec.
OutputSpec
Initializer for OutputSpec.
TabularObjective
Initializer for TabularObjective.