# CloudWatch Metrics
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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`