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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,
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 |
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.