The objective configuration for model monitoring, including the information needed to detect anomalies for one particular model.
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{ "trainingDataset": { object ( |
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trainingDataset |
Training dataset for models. This field has to be set only if TrainingPredictionSkewDetectionConfig is specified. |
trainingPredictionSkewDetectionConfig |
The config for skew between training data and prediction data. |
predictionDriftDetectionConfig |
The config for drift of prediction data. |
explanationConfig |
The config for integrating with Vertex Explainable AI. |
TrainingDataset
Training Dataset information.
JSON representation |
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{ "dataFormat": string, "targetField": string, "loggingSamplingStrategy": { object ( |
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dataFormat |
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 |
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 |
Strategy to sample data from Training Dataset. If not set, we process the whole dataset. |
Union field
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dataset |
The resource name of the Dataset used to train this Model. |
gcsSource |
The Google Cloud Storage uri of the unmanaged Dataset used to train this Model. |
bigquerySource |
The BigQuery table of the unmanaged Dataset used to train this Model. |
TrainingPredictionSkewDetectionConfig
The config for Training & Prediction data skew detection. It specifies the training dataset sources and the skew detection parameters.
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{ "skewThresholds": { string: { object ( |
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skewThresholds |
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 |
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 |
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. |
PredictionDriftDetectionConfig
The config for Prediction data drift detection.
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{ "driftThresholds": { string: { object ( |
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driftThresholds |
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 |
Key is the feature name and value is the threshold. The threshold here is against attribution score distance between different time windows. |
defaultDriftThreshold |
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. |
ExplanationConfig
The config for integrating with Vertex Explainable AI. Only applicable if the Model has explanationSpec populated.
JSON representation |
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{
"enableFeatureAttributes": boolean,
"explanationBaseline": {
object ( |
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enableFeatureAttributes |
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 |
Predictions generated by the BatchPredictionJob using baseline dataset. |
ExplanationBaseline
Output from BatchPredictionJob
for Model Monitoring baseline dataset, which can be used to generate baseline attribution scores.
JSON representation |
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{ "predictionFormat": enum ( |
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predictionFormat |
The storage format of the predictions generated BatchPrediction job. |
Union field destination . 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: |
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gcs |
Cloud Storage location for BatchExplain output. |
bigquery |
BigQuery location for BatchExplain output. |
PredictionFormat
The storage format of the predictions generated BatchPrediction job.
Enums | |
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PREDICTION_FORMAT_UNSPECIFIED |
Should not be set. |
JSONL |
Predictions are in JSONL files. |
BIGQUERY |
Predictions are in BigQuery. |