ModelMonitoringObjectiveConfig

The objective configuration for model monitoring, including the information needed to detect anomalies for one particular model.

Fields
trainingDatasetobject (TrainingDataset)

Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified.

trainingPredictionSkewDetectionConfigobject (TrainingPredictionSkewDetectionConfig)

The config for skew between training data and prediction data.

predictionDriftDetectionConfigobject (PredictionDriftDetectionConfig)

The config for drift of prediction data.

explanationConfigobject (ExplanationConfig)

The config for integrating with Vertex Explainable AI.

JSON representation
{"trainingDataset":{object (TrainingDataset)},"trainingPredictionSkewDetectionConfig":{object (TrainingPredictionSkewDetectionConfig)},"predictionDriftDetectionConfig":{object (PredictionDriftDetectionConfig)},"explanationConfig":{object (ExplanationConfig)}}

TrainingDataset

Training Dataset information.

Fields
dataFormatstring

data format of the dataset, only applicable if the input is from Google Cloud Storage. The possible formats are:

"tf-record" The source file is a TFRecord file.

"csv" The source file is a CSV file. "jsonl" The source file is a JSONL file.

targetFieldstring

The target field name the model is to predict. This field will be excluded when doing Predict and (or) Explain for the training data.

loggingSamplingStrategyobject (SamplingStrategy)

Strategy to sample data from Training Dataset. If not set, we process the whole dataset.

data_sourceUnion type
data_source can be only one of the following:
datasetstring

The resource name of the Dataset used to train this Model.

gcsSourceobject (GcsSource)

The Google Cloud Storage uri of the unmanaged Dataset used to train this Model.

bigquerySourceobject (BigQuerySource)

The BigQuery table of the unmanaged Dataset used to train this Model.

JSON representation
{"dataFormat":string,"targetField":string,"loggingSamplingStrategy":{object (SamplingStrategy)},// data_source"dataset":string,"gcsSource":{object (GcsSource)},"bigquerySource":{object (BigQuerySource)}// Union type}

TrainingPredictionSkewDetectionConfig

The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters.

Fields
skewThresholdsmap (key: string, value: object (ThresholdConfig))

Key is the feature name and value is the threshold. If a feature needs to be monitored for skew, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between the training and prediction feature.

attributionScoreSkewThresholdsmap (key: string, value: object (ThresholdConfig))

Key is the feature name and value is the threshold. The threshold here is against attribution score distance between the training and prediction feature.

defaultSkewThresholdobject (ThresholdConfig)

Skew anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

JSON representation
{"skewThresholds":{string:{object (ThresholdConfig)},...},"attributionScoreSkewThresholds":{string:{object (ThresholdConfig)},...},"defaultSkewThreshold":{object (ThresholdConfig)}}

PredictionDriftDetectionConfig

The config for Prediction data drift detection.

Fields
driftThresholdsmap (key: string, value: object (ThresholdConfig))

Key is the feature name and value is the threshold. If a feature needs to be monitored for drift, a value threshold must be configured for that feature. The threshold here is against feature distribution distance between different time windws.

attributionScoreDriftThresholdsmap (key: string, value: object (ThresholdConfig))

Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows.

defaultDriftThresholdobject (ThresholdConfig)

Drift anomaly detection threshold used by all features. When the per-feature thresholds are not set, this field can be used to specify a threshold for all features.

JSON representation
{"driftThresholds":{string:{object (ThresholdConfig)},...},"attributionScoreDriftThresholds":{string:{object (ThresholdConfig)},...},"defaultDriftThreshold":{object (ThresholdConfig)}}

ExplanationConfig

The config for integrating with Vertex Explainable AI. Only applicable if the Model has explanationSpec populated.

Fields
enableFeatureAttributesboolean

If want to analyze the Vertex Explainable AI feature attribute scores or not. If set to true, Vertex AI will log the feature attributions from explain response and do the skew/drift detection for them.

explanationBaselineobject (ExplanationBaseline)

Predictions generated by the BatchPredictionJob using baseline dataset.

JSON representation
{"enableFeatureAttributes":boolean,"explanationBaseline":{object (ExplanationBaseline)}}

ExplanationBaseline

Output fromBatchPredictionJob for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores.

Fields
predictionFormatenum (PredictionFormat)

The storage format of the predictions generated BatchPrediction job.

destinationUnion type
The configuration specifying of BatchExplain job output. This can be used to generate the baseline of feature attribution scores.destination can be only one of the following:
gcsobject (GcsDestination)

Cloud Storage location for BatchExplain output.

bigqueryobject (BigQueryDestination)

BigQuery location for BatchExplain output.

JSON representation
{"predictionFormat":enum (PredictionFormat),// destination"gcs":{object (GcsDestination)},"bigquery":{object (BigQueryDestination)}// Union type}

PredictionFormat

The storage format of the predictions generated BatchPrediction job.

Enums
PREDICTION_FORMAT_UNSPECIFIEDShould not be set.
JSONLPredictions are in JSONL files.
BIGQUERYPredictions are in BigQuery.

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Last updated 2025-06-27 UTC.