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Inside, we can see the UI base on MLFlow tracking interface, where the user can work with different experiments and executions and transform them to productive models.

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Next steps

• Full integration with Platform security: each user will see his experiments and will be able to share them with other users.

• Serving Models as Platform APIS

• Deployment of models as containers

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