Amazon SageMaker now integrates with Amazon DataZone to help unify governance across data and ML assets

[Amazon SageMaker](https://aws.amazon.com/sagemaker/) now integrates with [Amazon DataZone](https://aws.amazon.com/datazone/) making it easier for customers to access machine learning (ML) infrastructure, data and ML assets. This integration will unify data governance across data and ML workflows. ML administrators can setup the infrastructure controls and permissions for ML projects in Amazon DataZone. Project members can collaborate on business use cases and share assets with one another. Data scientists and ML engineers can then create a SageMaker environment and kick start their development process inside SageMaker Studio. Data scientists and ML engineers can also search, discover, and subscribe to data and ML assets in their business catalog within SageMaker Studio. They can consume these assets for ML tasks such as data preparation, model training, and feature engineering in SageMaker Studio and SageMaker Canvas. Upon completing the ML tasks, data scientists and ML engineers can publish data, models, and feature groups to the business catalog for governance and discoverability. This integration is supported in the following [AWS Regions](https://aws.amazon.com/about-aws/global-infrastructure/regional-product-services/) where SageMaker and Amazon DataZone are available: Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), Europe (Ireland), Europe (Stockholm), South America (São Paulo), US East (Ohio), US West (Oregon), and US East (N. Virginia), To learn more, see the [Amazon SageMaker ML governance web page](https://aws.amazon.com/sagemaker/ml-governance/) and the [Amazon SageMaker developer guide](https://docs.aws.amazon.com/sagemaker/latest/dg/sm-assets-user-guide.html).