Amazon SageMaker Studio offers integrated development environments (IDEs) for machine learning (ML) development. Studio provides tools for data preparation, experimentation and production to boost productivity. Studio users can now run SageMaker processing, training, inference and batch transform jobs locally on their Studio IDE instance. Users can also build and test SageMaker compatible Docker images locally in Studio IDEs.ENnbsp;

Data scientists can iteratively develop ML models and debug code changes quickly without leaving their IDE or waiting for remote compute resources. Users can run small-scale jobs locally to test implementations and inspect outputs before running full jobs in the cloud. This optimizes workflows by providing instant feedback on code changes and catching issues early without waiting for cloud resources.ENnbsp;

In addition, Studio Local Mode now provides Docker build and run capabilities. Users can build Docker images with their model code and dependencies right within Studio. This simplifies container creation by avoiding external Docker setup and build steps. Once built, containers can be run locally to validate implementations before deploying to the cloud. Building and testing containers in Studio improves developer productivity and accelerates the path to production. The Docker build feature also enables reuse of containers across environments. Containers built locally can be deployed unchanged to SageMaker for training and hosting. This consistency eliminates issues that arise from differences between local and cloud environments.ENnbsp;

This feature is now available in all regions where Amazon SageMaker is available. To learn more, see SageMaker Studio Local Mode and SageMaker Studio Docker Support.ENnbsp;