Amazon SageMaker Model Monitor prebuilt container
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.
SageMaker AI provides a built-in image called sagemaker-model-monitor-analyzer that
provides you with a range of model monitoring capabilities, including constraint suggestion,
statistics generation, constraint validation against a baseline, and emitting Amazon CloudWatch
metrics. This image is based on Spark version 3.3.0 and is built with Deequ
Note
You can not pull the built-in sagemaker-model-monitor-analyzer image
directly. You can use the sagemaker-model-monitor-analyzer image when you
submit a baseline processing or monitoring job using one of the AWS SDKs.
Use the SageMaker Python SDK (see image_uris.retrieve in the SageMaker AI Python
SDK reference guide
<ACCOUNT_ID>.dkr.ecr.<REGION_NAME>.amazonaws.com/sagemaker-model-monitor-analyzer
For example:
159807026194.dkr.ecr.us-west-2.amazonaws.com/sagemaker-model-monitor-analyzer
If you are in an AWS region in China, the prebuilt images for SageMaker Model Monitor can be accessed as follows:
<ACCOUNT_ID>.dkr.ecr.<REGION_NAME>.amazonaws.com.rproxy.govskope.ca.cn/sagemaker-model-monitor-analyzer
For account IDs and AWS Region names, see Docker Registry Paths and Example Code.
To write your own analysis container, see the container contract described in Custom monitoring schedules.
