Amazon SageMaker Studio is the first fully integrated development environment (IDE) for machine learning (ML). It provides a single, web-based visual interface where you can perform all ML development steps, including preparing data and building, training, and deploying models.
Within an Amazon SageMaker Domain, users can provision a personal Amazon SageMaker Studio IDE application, which runs a free JupyterServer with built‑in integrations to examine Amazon SageMaker Experiments, orchestrate Amazon SageMaker Pipelines, and much more. Users only pay for the flexible compute on their notebook kernels. These personal applications automatically mount a respective user’s private Amazon Elastic File System (Amazon EFS) home directory so they can keep code, data, and other files isolated from other users. Amazon SageMaker Studio already supports sharing of notebooks between private applications, but the asynchronous mechanism can slow down the iteration process.
Now with shared spaces in Amazon SageMaker Studio, users can organize collaborative ML endeavors

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