Posted On: Nov 30, 2022
Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) that enables ML practitioners to perform every step of the machine learning workflow, from preparing data to building, training, tuning, and deploying models. Today, we are announcing new capabilities in SageMaker Studio to accelerate real time collaboration across ML teams.
By creating shared spaces in SageMaker Studio, users can now access, read, edit, and share the same notebooks in real time. All resources in a shared space are filtered and tagged, making it easier to focus on ML projects and manage costs. Further, administrators now can provision multiple SageMaker domains in a region in order to separate different lines of business within a single AWS account. Finally, users can now configure a list of suggested Git repository URLs at the SageMaker domain or user profile level to aid collaboration using version control.