MLflow is an open-source platform designed for the end-to-end machine learning lifecycle, providing a centralized location for storing and managing all machine learning models, data, and metadata about model experiments. It comprises four main components: Tracking, Registry, Models, and Projects, each catering to a specific aspect of the machine learning pipeline. MLflow improves collaboration among data scientists and MLOps teams by leveraging features such as version control, metadata management, and access control, streamlining the process of creating and using machine learning models. The platform also offers tools for tracking and logging experiments, packaging and deploying machine learning models, managing dependencies and reproducibility, and ensuring efficient model management throughout the lifecycle. MLflow Registry provides a centralized location for storing, managing, and sharing machine learning models, enabling version control and collaboration across different teams and organizations. Finally, MLflow Projects offers a standardized method for packaging and sharing code, data, and environments across machine learning workflows, facilitating seamless reproduction and collaboration on experiments.