Mlflow

MLflow is an open-source platform designed to manage the end-to-end lifecycle of machine learning models, from experimentation to production. It provides four primary components: Tracking, Projects, Models, and Model Registry. With MLflow Tracking, data scientists and engineers can log parameters, metrics, artifacts, and source code for each run, enabling reproducible experiments and systematic comparison of model performance. MLflow Projects standardize how code is packaged and executed, using a simple configuration format to define dependencies and entry points, which supports consistent runs across different environments.

MLflow Models offers a unified way to package models in multiple flavors (such as Python function, scikit-learn, TensorFlow, PyTorch, and others) so they can be deployed to diverse serving platforms, including local REST endpoints, batch inference jobs, or cloud environments. The Model Registry centralizes model versioning, stage transitions (e.g., staging, production), and annotations, supporting governance and collaboration across teams. Typical use cases include hyperparameter tuning workflows, tracking and comparing experiments across teams, managing multiple model versions for A/B testing, and deploying models reliably into production systems. MLflow integrates with many popular ML libraries and frameworks, making it suitable for organizations seeking structured, scalable, and auditable machine learning operations.