MLflow is an open-source platform designed to track, maintain and monitor the entire machine learning workflow. Its main function is to ensure reproducibility and consistency in the results of a machine learning model across multiple uses and different scenarios.
MLflow is divided into four modules: Tracking, Projects, Model Packaging and Model Registry. Tracking involves experimentation and iterative execution monitoring; Projects ensures model reproducibility; Model Packaging is where the best model is made into a deployable package; and Model Registry manages and records the best models, capturing necessary metadata.
The Model Registry also allows for quick comparison between models, capturing changes and enabling evaluation of 'data drift'. It allows for the retraining of models with new data, and provides a platform to manage and compare models effectively.
The MLflow platform has a user interface that enables users to track and maintain their machine learning models, including capturing logs and parameters. It supports various languages like Python, Java, and Scala, and is developed by Databricks, which also maintains the Spark open-source community platform.
The ultimate goal of MLflow is to provide continuous training capability for machine learning models, accommodating new data and patterns, and maintaining a consistent and effective workflow. It bridges the work of data engineers, data scientists and machine learning engineers, allowing them to collaborate in preparing, training, and deploying models.

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