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    ModelMonitoringObjectiveConfig

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

    Fields
    trainingDataset object ( TrainingDataset )

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

    trainingPredictionSkewDetectionConfig object ( TrainingPredictionSkewDetectionConfig )

    The config for skew between training data and prediction data.

    predictionDriftDetectionConfig object ( PredictionDriftDetectionConfig )

    The config for drift of prediction data.

    explanationConfig object ( 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
    dataFormat string

    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.

    targetField string

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

    loggingSamplingStrategy object ( SamplingStrategy )

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

    data_source Union type
    data_source can be only one of the following:
    dataset string

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

    gcsSource object ( GcsSource )

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

    bigquerySource object ( 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
    skewThresholds map (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.

    attributionScoreSkewThresholds map (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.

    defaultSkewThreshold object ( 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
    driftThresholds map (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.

    attributionScoreDriftThresholds map (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.

    defaultDriftThreshold object ( 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
    enableFeatureAttributes boolean

    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.

    explanationBaseline object ( ExplanationBaseline )

    Predictions generated by the BatchPredictionJob using baseline dataset.

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

    ExplanationBaseline

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

    Fields
    predictionFormat enum ( PredictionFormat )

    The storage format of the predictions generated BatchPrediction job.

    destination Union 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:
    gcs object ( GcsDestination )

    Cloud Storage location for BatchExplain output.

    bigquery object ( 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_UNSPECIFIED Should not be set.
    JSONL Predictions are in JSONL files.
    BIGQUERY Predictions are in BigQuery.
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