MLflow is an open-source platform designed to manage the complete machine learning lifecycle, encompassing experimentation, reproducibility, and deployment. It provides a suite of tools that facilitate tracking experiments, packaging code into reproducible runs, and sharing and deploying models. With its four main components—MLflow Tracking, MLflow Projects, MLflow Models, and MLflow Registry—users can efficiently log parameters, metrics, and artifacts, create pipelines, and manage model versions. This versatility makes MLflow compatible with any machine learning library, streamlining the workflow for data scientists and developers alike, ultimately enhancing productivity and collaboration in machine learning projects.