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What is MLflow used for?

mlFlow is a framework that supports the machine learning lifecycle. This means that it has components to monitor your model during training and running, the ability to store models, load the model in production code, and create a pipeline.
What is MLflow used for?

MLflow is an open-source platform for managing the end-to-end machine learning lifecycle. It tackles four primary functions:

  • Tracking experiments to record and compare parameters and results (MLflow Tracking).
  • Packaging ML code in a reusable, reproducible form to share with other data scientists or transfer to production (MLflow Projects).
  • Managing and deploying models from a variety of ML libraries to a variety of model serving and inference platforms (MLflow Models).
  • Providing a central model store to collaboratively manage the full lifecycle of an MLflow Model, including model versioning, stage transitions, and annotations (MLflow Model Registry).

MLflow is library-agnostic. You can use it with any machine learning library, and any programming language, since all functions, are accessible through a REST API and CLI. For convenience, the project also includes a Python API, R API, and Java API.

MLflow Documentation — MLflow 1.19.0 documentation
https://www.mlflow.org/docs/latest/index.html

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