Amazon SageMaker Studio is a single web-based interface with comprehensive machine learning (ML) tools and a choice of fully managed integrated development environments (IDEs) to perform every step of ML development, from preparing data to building, training, deploying, and managing ML models. Amazon EFS is a simple, serverless, set-and-forget, elastic file system that makes it easy to set up, scale, and cost-optimize file storage in the AWS Cloud. Today, we are excited to announce a new capability that allows you to bring you own EFS volume to access your large ML datasets or shared code from IDEs such as JupyterLab and Code Editor in SageMaker Studio.

You can now make pre-existing EFS volumes available to multiple users in SageMaker within their IDEs to allow them to access common datasets on a filesystem without requiring data movement thus saving time, effort, and cost.
With this, you can also share notebooks, code and data with your colleagues to boost productivity and collaborate faster on your ML workflows. Further, you can access the same EFS volume across different steps of ML workflow such as model building and training enabling you to iterate and experiment quickly.

This capability is available in all Amazon Web Services (AWS) regions where Amazon SageMaker Studio is currently available, except China and the AWS GovCloud (US) regions. To learn more, please refer to SageMaker Studio documentation.
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