components Model Monitoring documentation - Azure/azureml-assets GitHub Wiki
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action_analyzer_correlation_test
Perform correlation test on different groups to generate actions.
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action_analyzer_identify_problem_traffic
Separate bad queries into different groups.
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action_analyzer_metrics_calculation
Calculate futher metrics for generating actions.
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action_analyzer_output_actions
Merge and output actions.
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Compute data drift metrics given a baseline and a deployment's model data input.
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Computes the data drift between a baseline and production data assets.
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Compute data quality metrics leveraged by the data quality monitor.
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Compute data statistics leveraged by the data quality monitor.
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Join baseline and target data quality metrics into a single output.
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Computes the data quality of a target dataset with reference to a baseline.
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feature_attribution_drift_compute_metrics
Feature attribution drift using model monitoring.
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feature_attribution_drift_signal_monitor
Computes the feature attribution between a baseline and production data assets.
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Feature importance for model monitoring.
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Filters the raw span log based on the window provided, and aggregates it to trace level.
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genai_token_statistics_compute_metrics
Compute token statistics metrics.
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genai_token_statistics_signal_monitor
Computes the token and cost metrics over LLM outputs.
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generation_safety_quality_signal_monitor
Computes the content generation safety metrics over LLM outputs.
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gsq_annotation_compute_histogram
Compute annotation histogram given a deployment's model data input.
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gsq_annotation_compute_metrics
Compute annotation metrics given a deployment's model data input.
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Adapt data to fit into GSQ component.
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model_data_collector_preprocessor
Filters the data based on the window provided.
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Generate and output actions to the default datastore.
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Generate and output actions
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model_monitor_azmon_metric_publisher
Azure Monitor Publisher for the computed model monitor metrics.
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model_monitor_compute_histogram
Compute a histogram given an input data and associated histogram buckets.
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model_monitor_compute_histogram_buckets
Compute histogram buckets given up to two datasets.
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Creates the model monitor metric manifest.
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Joins two data assets on the given columns for model monitor.
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model_monitor_evaluate_metrics_threshold
Evaluate signal metrics against the threshold provided in the monitoring signal.
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model_monitor_feature_selector
Selects features to compute signal metrics on.
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model_monitor_metric_outputter
Output the computed model monitor metrics.
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Output the computed model monitor metrics to the default datastore.
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model_performance_compute_metrics
Compute model performance metrics leveraged by the model performance monitor.
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model_performance_signal_monitor
Computes the model performance
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prediction_drift_signal_monitor
Computes the prediction drift between a baseline and a target data assets.
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token_statistics_compute_metrics
Compute token statistics metrics.