MLflow is an open source platform to manage the ML lifecycle, including experimentation, reproducibility, deployment, and a central model registry. MLflow currently offers four components:

1.) MLflow Tracking - Record and query experiments: code, data, config, and results
2.) MLflow Projects - Package data science code in a format to reproduce runs on any platform
3.) MLflow Models - Deploy machine learning models in diverse serving environments
4.) Model Registry - Store, annotate, discover, and manage models in a central repository


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MLflow

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