components Model Monitoring documentation

Model Monitoring

Components in this category

---

• data_drift_compute_metrics

  Compute data drift metrics given a baseline and a deployment's model data input.

• data_drift_signal_monitor

  Computes the data drift between a baseline and production data assets.

• data_quality_compute_metrics

  Compute data quality metrics leveraged by the data quality monitor.

• data_quality_data_statistics

  Compute data statistics leveraged by the data quality monitor.

• data_quality_metrics_joiner

  Join baseline and target data quality metrics into a single output.

• data_quality_signal_monitor

  Computes the data quality of a target dataset with reference to a baseline.

• feature_attribution_drift_compute_metrics

  Feature attribution drift using model monitoring.

• feature_attribution_drift_signal_monitor

  Computes the feature attribution between a baseline and production data assets.

• feature_importance_metrics

  Feature importance for model monitoring.

• model_data_collector_preprocessor

  Filters the data based on the window provided.

• model_monitor_compute_histogram

  Compute a histogram given an input data and associated histogram buckets.

• model_monitor_compute_histogram_buckets

  Compute histogram buckets given up to two datasets.

• model_monitor_create_manifest

  Creates the model monitor metric manifest.

• model_monitor_evaluate_metrics_threshold

  Evaluate the metrics against the threshold provided in the model monitor.

• model_monitor_feature_selector

  Selects features to compute signal metrics on.

• model_monitor_output_metrics

  Output the computed model monitor metrics to the default datastore.

• prediction_drift_signal_monitor

  Computes the prediction drift between a baseline and a target data assets.