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While Amazon SageMaker offers shared spaces for collaboration, there are some limitations to be aware of regarding SageMaker Canvas models and Data Wrangler flows.
Shared spaces in SageMaker Studio Classic and JupyterLab consist of a shared Jupyter application and a shared directory where all user profiles in a domain have access. These spaces allow for collaboration on resources like notebooks, files, experiments, and models with features such as automatic tagging and real-time co-editing.
However, it's important to note that shared spaces do not support Amazon SageMaker Data Wrangler. This means you cannot directly share your Data Wrangler flows through shared spaces.
For sharing models across accounts, SageMaker offers the Model Registry with AWS Resource Access Manager (RAM) sharing capabilities. This allows you to securely share and discover machine learning models across different AWS accounts. You can create a model package group in one account and share it with other accounts using the SageMaker Studio console or APIs.
Another option for collaboration is SageMaker Assets, which enables controlled access to assets like models or data tables within an organization. With SageMaker Assets, you can share AWS Glue tables and model packages with other users in a few clicks, providing precisely scoped permissions without administrator intervention.
For broader collaboration needs, SageMaker allows you to configure domains that provide isolated portals for data teams to collaborate. Within these domains, you can create projects backed by GitHub repositories, enabling user collaboration with version control and access to development tools.
Sources
Collaboration with shared spaces - Amazon SageMaker AI
Centralize model governance with SageMaker Model Registry Resource Access Manager sharing | AWS Machine Learning Blog
Guidance for Collaborative, Unified Data and AI Development on AWS
Controlled access to assets with Amazon SageMaker Assets - Amazon SageMaker
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