Introducing new ML governance tools for Amazon SageMaker

Today, we are excited to announce three new purpose-built tools for Amazon SageMaker to improve governance of your machine learning (ML) projects with simplified access control and enhanced transparency across your ML model’s lifecycle. With Amazon SageMaker Role Manager, you can define minimum permissions for users in minutes and onboard new users faster. SageMaker Role Manager simplifies the permission setting for ML activities and automatically generates a custom policy based on your specific needs. With Amazon SageMaker Model Cards, you can create a single source of truth for model information by centralizing and standardizing documentation throughout the model lifecycle. You can record details such as purpose and performance goals while SageMaker Model Cards autopopulates training details to accelerate the process. Once the models are deployed, Amazon SageMaker Model Dashboard gives you unified monitoring across all your models by providing deviations from expected behavior, automated alerts, and troubleshooting to improve model performance. All three features are now available in all [AWS Regions](/about-aws/global-infrastructure/regional-product-services/) where Amazon SageMaker is currently available, excluding China. To get started, refer to the ML governance with Amazon SageMaker [webpage](/sagemaker/ml-governance/) and technical [documentation](https://docs.aws.amazon.com/sagemaker/latest/dg/governance.html). To learn more, read our [blog post](https://aws.amazon.com/blogs/aws/new-ml-governance-tools-for-amazon-sagemaker-simplify-access-control-and-enhance-transparency-over-your-ml-projects).