Inspect Reports for Feature Attribute Drift in Production Models - Amazon SageMaker AI
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Inspect Reports for Feature Attribute Drift in Production Models

Note

After careful consideration, we have made the decision to close new customer access to Amazon Sagemaker Model Monitor, effective 7/30/26. Existing customers can continue to use the service as normal. AWS continues to invest in security and availability improvements for Model Monitor, but we do not plan to introduce new features. For more information, see Amazon SageMaker Model Monitor availability change.

After the schedule that you set up is started by default, you need to wait for the its first execution to start, and then stop the schedule to avoid incurring charges.

To inspect the reports, use the following code:

schedule_desc = model_explainability_monitor.describe_schedule()
execution_summary = schedule_desc.get("LastMonitoringExecutionSummary")
if execution_summary and execution_summary["MonitoringExecutionStatus"] in ["Completed", "CompletedWithViolations"]:
    last_model_explainability_monitor_execution = model_explainability_monitor.list_executions()[-1]
    last_model_explainability_monitor_execution_report_uri = last_model_explainability_monitor_execution.output.destination
    print(f'Report URI: {last_model_explainability_monitor_execution_report_uri}')
    last_model_explainability_monitor_execution_report_files = sorted(S3Downloader.list(last_model_explainability_monitor_execution_report_uri))
    print("Found Report Files:")
    print("\n ".join(last_model_explainability_monitor_execution_report_files))
else:
    last_model_explainability_monitor_execution = None
    print("====STOP==== \n No completed executions to inspect further. Please wait till an execution completes or investigate previously reported failures.")

If there are any violations compared to the baseline, they are listed here:

if last_model_explainability_monitor_execution:
    model_explainability_violations = last_model_explainability_monitor_execution.constraint_violations()
    if model_explainability_violations:
        print(model_explainability_violations.body_dict)

If your model is deployed to a real-time endpoint, you can see visualizations in SageMaker Studio of the analysis results and CloudWatch metrics by choosing the Endpoints tab, and then double-clicking the endpoint.