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.
Fields
features[]string
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.
Alert triggered condition.conditioncan be only one of the following:
thresholdnumber
A condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold.
JSON representation
{// condition"threshold":number// Union type}
FeatureAttributionSpec
feature attribution monitoring spec.
Fields
features[]string
feature names interested in monitoring. These should be a subset of the input feature names specified in the monitoring schema. If the field is not specified all features outlied in the monitoring schema will be used.
The config of resources used by the Model Monitoring during the batch explanation for non-AutoML models. If not set,n1-standard-2machine type will be used by default.
[[["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 2025-06-27 UTC."],[],[],null,["# TabularObjective\n\nTabular monitoring objective.\nFields `featureDriftSpec` `object (`[DataDriftSpec](/vertex-ai/docs/reference/rest/v1beta1/TabularObjective#DataDriftSpec)`)` \nInput feature distribution drift monitoring spec.\n`predictionOutputDriftSpec` `object (`[DataDriftSpec](/vertex-ai/docs/reference/rest/v1beta1/TabularObjective#DataDriftSpec)`)` \nPrediction output distribution drift monitoring spec.\n`featureAttributionSpec` `object (`[FeatureAttributionSpec](/vertex-ai/docs/reference/rest/v1beta1/TabularObjective#FeatureAttributionSpec)`)` \nfeature attribution monitoring spec. \n\nDataDriftSpec\n-------------\n\ndata 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.\nFields `features[]` `string` \nfeature 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.\n`categoricalMetricType` `string` \nSupported metrics type: \\* l_infinity \\* jensen_shannon_divergence\n`numericMetricType` `string` \nSupported metrics type: \\* jensen_shannon_divergence\n`defaultCategoricalAlertCondition` `object (`[ModelMonitoringAlertCondition](/vertex-ai/docs/reference/rest/v1beta1/TabularObjective#ModelMonitoringAlertCondition)`)` \nDefault alert condition for all the categorical features.\n`defaultNumericAlertCondition` `object (`[ModelMonitoringAlertCondition](/vertex-ai/docs/reference/rest/v1beta1/TabularObjective#ModelMonitoringAlertCondition)`)` \nDefault alert condition for all the numeric features.\n`featureAlertConditions` `map (key: string, value: object (`[ModelMonitoringAlertCondition](/vertex-ai/docs/reference/rest/v1beta1/TabularObjective#ModelMonitoringAlertCondition)`))` \nPer feature alert condition will override default alert condition. \n\nModelMonitoringAlertCondition\n-----------------------------\n\nMonitoring alert triggered condition.\nFields \n`condition` `Union type` \nAlert triggered condition. `condition` can be only one of the following:\n`threshold` `number` \nA condition that compares a stats value against a threshold. Alert will be triggered if value above the threshold. \n\nFeatureAttributionSpec\n----------------------\n\nfeature attribution monitoring spec.\nFields `features[]` `string` \nfeature names interested in monitoring. These should be a subset of the input feature names specified in the monitoring schema. If the field is not specified all features outlied in the monitoring schema will be used.\n`defaultAlertCondition` `object (`[ModelMonitoringAlertCondition](/vertex-ai/docs/reference/rest/v1beta1/TabularObjective#ModelMonitoringAlertCondition)`)` \nDefault alert condition for all the features.\n`featureAlertConditions` `map (key: string, value: object (`[ModelMonitoringAlertCondition](/vertex-ai/docs/reference/rest/v1beta1/TabularObjective#ModelMonitoringAlertCondition)`))` \nPer feature alert condition will override default alert condition.\n`batchExplanationDedicatedResources` `object (`[BatchDedicatedResources](/vertex-ai/docs/reference/rest/v1beta1/BatchDedicatedResources)`)` \nThe config of resources used by the Model Monitoring during the batch explanation for non-AutoML models. If not set, `n1-standard-2` machine type will be used by default."]]
