Amazon SageMaker Model Monitor prebuilt container - Amazon SageMaker AI

Amazon SageMaker Model Monitor prebuilt container

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 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) 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.goskope.com.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.