Machine learning (ML) teams need the flexibility to choose their integrated development environment (IDE) when working on a project. It allows you to have a productive developer experience and innovate at speed. You may even use multiple IDEs within a project. Amazon SageMaker lets ML teams choose to work from fully managed, cloud-based environments within Amazon SageMaker Studio, SageMaker Notebook Instances, or from your local machine using local mode.
SageMaker provides a one-click experience to Jupyter and RStudio to build, train, debug, deploy, and monitor ML models. In this post, we will also share a solution for hosting code-server on SageMaker.
With code-server, users can run VS Code on remote machines and access it in a web browser. For ML teams, hosting code-server on SageMaker provides minimal changes to a local development experience, and allows you to code from anywhere, on scalable cloud compute. With VS Code, you can also

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