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Experiment tracking
Artifact tracking
Model registry
Model serving

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle.

Use it when

  • You want to organize projects and runs and track your experiments (manual and automatic logging), artifacts, and data.
  • You want to keep track of your models with a model registry and serve them using integrations.
  • You want a platform that is non-opinionated and gives you flexibility.

Watch out

  • MLflow can track data but provides limited capability in terms of data versioning. You may have to integrate other tools.
  • MLflow's built-in model serving is quite limited. You will likely need to integrate with a third-party tool for a robust solution.

Example stacks

Airflow + MLflow stack


pip install mlflow