The open-source tool to simplify your ML model deployments_

  • Save your ML model with a Python call.

    Stick to your training workflow.

  • Model metadata is captured automatically.

    Use a human-readable YAML format for any ML framework.

  • Deploy models anywhere you want.

    Switch between deployment platforms with a single command.

  • Make Git a Model Registry

    MLEM is a core building block for Git-native ML model registries, combined with other tools like GTO or DVC.

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  • Git-native ML model development. Enable GitFlow and other software engineering best practices.
  • Automatically detect ML framework, Python requirements, and data schema.
  • Seamlessly integrating to your stack thanks to Unix philosophy: one tool solves one problem very well.