CloudWatch Metrics for Feature Drift Analysis - Amazon SageMaker AI

CloudWatch Metrics for Feature Drift Analysis

This guide shows CloudWatch metrics and their properties that you can use for feature attribute drift analysis in SageMaker Clarify. Feature attribute drift monitoring jobs compute and publish two types of metrics:

  • The global SHAP value of each feature.

    Note

    The name of this metric appends the feature name provided by the job analysis configuration to feature_. For example, feature_X is the global SHAP value for feature X.

  • The ExpectedValue of the metric.

These metrics are published to the following CloudWatch namespace:

  • For real-time endpoints: aws/sagemaker/Endpoints/explainability-metrics

  • For batch transform jobs: aws/sagemaker/ModelMonitoring/explainability-metrics

Each metric has the following properties:

  • Endpoint: The name of the monitored endpoint, if applicable.

  • MonitoringSchedule: The name of the schedule for the monitoring job.

  • ExplainabilityMethod: The method used to compute Shapley values. Choose KernelShap.

  • Label: The name provided by job analysis configuration label_headers, or a placeholder like label0.

  • ValueType: The type of the value returned by the metric. Choose either GlobalShapValues or ExpectedValue.

To stop the monitoring jobs from publishing metrics, set publish_cloudwatch_metrics to Disabled in the Environment map of model explainability job definition.