MLflow is an open-source platform designed to manage the complete end-to-end machine learning lifecycle. It provides tools for experimentation, reproducibility, and deployment, making it a versatile choice for data scientists and machine learning practitioners. The platform accommodates various machine learning libraries, allowing users to track experiments, manage models, and streamline the transition from development to production.

The main components of MLflow include MLflow Tracking, which allows users to log and manage their machine learning experiments, and the MLflow Model Registry, a centralized repository for organizing and sharing models. This integration facilitates collaboration among teams and simplifies the process of moving models from experimentation to production environments ¹³⁴.

By using MLflow, practitioners can avoid common pitfalls, such as losing track of model performance and hyperparameters, ultimately enhancing productivity and efficiency in machine learning projects ¹⁴.