Accelerate ML workflows with Amazon SageMaker Studio Local Mode and Docker support

This article discusses two new capabilities in Amazon SageMaker Studio that accelerate machine learning (ML) workflows: Local Mode and Docker support.

Specifically, the article covers:

• SageMaker Studio Local Mode, which enables running SageMaker training, inference, batch transform, and processing jobs directly on a JupyterLab or Code Editor notebook instance without requiring remote compute resources
• Docker support in SageMaker Studio notebooks, allowing developers to build and run Docker containers locally on their notebook instance
• Step-by-step instructions for setting up Local Mode and Docker support, and running examples demonstrating their usage
• Tips for using SageMaker Local Mode and configuring Docker installation as a Lifecycle Configuration
• Conclusion emphasizing how these new capabilities optimize ML workflows and improve productivity