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CloudWatch Metrics - Amazon SageMaker AI

CloudWatch Metrics

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.

You can use the built-in Amazon SageMaker Model Monitor container for CloudWatch metrics. When the emit_metrics option is Enabled in the baseline constraints file, SageMaker AI emits these metrics for each feature/column observed in the dataset in the following namespace:

  • For real-time endpoints: /aws/sagemaker/Endpoints/data-metric namespace with EndpointName and ScheduleName dimensions.

  • For batch transform jobs: /aws/sagemaker/ModelMonitoring/data-metric namespace with MonitoringSchedule dimension.

For numerical fields, the built-in container emits the following CloudWatch metrics:

  • Metric: Max → query for MetricName: feature_data_{feature_name}, Stat: Max

  • Metric: Min → query for MetricName: feature_data_{feature_name}, Stat: Min

  • Metric: Sum → query for MetricName: feature_data_{feature_name}, Stat: Sum

  • Metric: SampleCount → query for MetricName: feature_data_{feature_name}, Stat: SampleCount

  • Metric: Average → query for MetricName: feature_data_{feature_name}, Stat: Average

For both numerical and string fields, the built-in container emits the following CloudWatch metrics:

  • Metric: Completeness → query for MetricName: feature_non_null_{feature_name}, Stat: Sum

  • Metric: Baseline Drift → query for MetricName: feature_baseline_drift_{feature_name}, Stat: Sum