# Amazon SageMaker Model Monitor prebuilt container
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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](https://github.com/awslabs/deequ) version 2.0.2.

**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](https://sagemaker.readthedocs.io/en/stable/api/utility/image_uris.html)) to generate the ECR image URI for you, or specify the ECR image URI directly. The prebuilt image for SageMaker Model Monitor can be accessed as follows:

`<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.us.cn/sagemaker-model-monitor-analyzer`

For account IDs and AWS Region names, see [Docker Registry Paths and Example Code](https://docs.aws.amazon.com/sagemaker/latest/dg-ecr-paths/sagemaker-algo-docker-registry-paths).

To write your own analysis container, see the container contract described in [Custom monitoring schedules](model-monitor-custom-monitoring-schedules.md).