ML practitioners can create a shared workspace where teammates can read and edit SageMaker Studio notebooks together. By using the shared paces, teammates can coedit the same notebook file, run notebook code simultaneously, and review the results together to eliminate back and forth and streamline collaboration. In the shared spaces, ML teams will have built-in support for services like BitBucket and AWS CodeCommit, so they can easily manage different versions of their notebook and compare changes over time. Any resources created from within the notebooks, such as experiments and ML models, are automatically saved and associated with the specific workspace where they were created so teams can more easily stay organized and accelerate ML model development.