Class DataDriftSpec (1.153.1)

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

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.

Attributes
Name	Description
features	MutableSequence[str] Feature names / Prediction output names interested in monitoring. These should be a subset of the input feature names or prediction output names specified in the monitoring schema. If the field is not specified all features / prediction outputs outlied in the monitoring schema will be used.
categorical_metric_type	str Supported metrics type: - l_infinity - jensen_shannon_divergence
numeric_metric_type	str Supported metrics type: - jensen_shannon_divergence
default_categorical_alert_condition	google.cloud.aiplatform_v1beta1.types.ModelMonitoringAlertCondition Default alert condition for all the categorical features.
default_numeric_alert_condition	google.cloud.aiplatform_v1beta1.types.ModelMonitoringAlertCondition Default alert condition for all the numeric features.
feature_alert_conditions	MutableMapping[str, google.cloud.aiplatform_v1beta1.types.ModelMonitoringAlertCondition] Per feature alert condition will override default alert condition.

Classes

FeatureAlertConditionsEntry

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

The abstract base class for a message.

Parameters
Name	Description
kwargs	dict Keys and values corresponding to the fields of the message.
mapping	Union[dict, .Message] A dictionary or message to be used to determine the values for this message.
ignore_unknown_fields	Optional(bool) If True, do not raise errors for unknown fields. Only applied if mapping is a mapping type or there are keyword parameters.

Methods

DataDriftSpec

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

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.