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.
List[str]
Optional. 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 not specified, all features / prediction outputs outlied in the
monitoring schema will be used.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-07 UTC."],[],[],null,["# Class DataDriftSpec (1.95.1)\n\nVersion latestkeyboard_arrow_down\n\n- [1.95.1 (latest)](/python/docs/reference/vertexai/latest/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.94.0](/python/docs/reference/vertexai/1.94.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.93.1](/python/docs/reference/vertexai/1.93.1/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.92.0](/python/docs/reference/vertexai/1.92.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.91.0](/python/docs/reference/vertexai/1.91.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.90.0](/python/docs/reference/vertexai/1.90.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.89.0](/python/docs/reference/vertexai/1.89.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.88.0](/python/docs/reference/vertexai/1.88.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.87.0](/python/docs/reference/vertexai/1.87.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.86.0](/python/docs/reference/vertexai/1.86.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.85.0](/python/docs/reference/vertexai/1.85.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.84.0](/python/docs/reference/vertexai/1.84.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.83.0](/python/docs/reference/vertexai/1.83.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.82.0](/python/docs/reference/vertexai/1.82.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.81.0](/python/docs/reference/vertexai/1.81.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.80.0](/python/docs/reference/vertexai/1.80.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.79.0](/python/docs/reference/vertexai/1.79.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.78.0](/python/docs/reference/vertexai/1.78.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.77.0](/python/docs/reference/vertexai/1.77.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.76.0](/python/docs/reference/vertexai/1.76.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.75.0](/python/docs/reference/vertexai/1.75.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.74.0](/python/docs/reference/vertexai/1.74.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.73.0](/python/docs/reference/vertexai/1.73.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.72.0](/python/docs/reference/vertexai/1.72.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.71.1](/python/docs/reference/vertexai/1.71.1/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.70.0](/python/docs/reference/vertexai/1.70.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.69.0](/python/docs/reference/vertexai/1.69.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.68.0](/python/docs/reference/vertexai/1.68.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.67.1](/python/docs/reference/vertexai/1.67.1/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.66.0](/python/docs/reference/vertexai/1.66.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.65.0](/python/docs/reference/vertexai/1.65.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.63.0](/python/docs/reference/vertexai/1.63.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.62.0](/python/docs/reference/vertexai/1.62.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.60.0](/python/docs/reference/vertexai/1.60.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec)\n- [1.59.0](/python/docs/reference/vertexai/1.59.0/vertexai.resources.preview.ml_monitoring.spec.DataDriftSpec) \n\n DataDriftSpec(\n features: typing.Optional[typing.List[str]] = None,\n categorical_metric_type: typing.Optional[str] = \"l_infinity\",\n numeric_metric_type: typing.Optional[str] = \"jensen_shannon_divergence\",\n default_categorical_alert_threshold: typing.Optional[float] = None,\n default_numeric_alert_threshold: typing.Optional[float] = None,\n feature_alert_thresholds: typing.Optional[typing.Dict[str, float]] = None,\n )\n\nData drift monitoring spec.\n\nData drift measures the distribution distance between the current dataset\nand a baseline dataset. A typical use case is to detect data drift between\nthe recent production serving dataset and the training dataset, or to\ncompare the recent production dataset with a dataset from a previous period.\n\n.. rubric:: Example\n\nfeature_drift_spec=DataDriftSpec(\nfeatures=\\[\"feature1\"\\]\ncategorical_metric_type=\"l_infinity\",\nnumeric_metric_type=\"jensen_shannon_divergence\",\ndefault_categorical_alert_threshold=0.01,\ndefault_numeric_alert_threshold=0.02,\nfeature_alert_thresholds={\"feature1\":0.02, \"feature2\":0.01},\n)"]]