MLflow

MLflow is an open source developer platform designed to help teams manage the end to end machine learning lifecycle. At its core it provides Experiment Tracking so you can log parameters, metrics, artifacts, and evaluation results for each run and compare them in a UI. This reduces guesswork when you are iterating on features, model architectures, or prompting strategies. MLflow also includes a Model Registry, a centralized store and set of APIs to manage versions, stages, and lifecycle actions for models. This supports collaboration across data scientists and engineers by creating a shared record of what is approved for testing or deployment. For deployment workflows, MLflow provides standard packaging and interfaces that can be used to move models between environments, and it is commonly run locally, on premises, or in cloud environments. The ecosystem includes a large open source community and hosted options from major platforms. MLflow.org also promotes a free and fully managed hosted MLflow experience to reduce setup friction, while retaining open source compatibility. For teams that prefer full control, self hosting is possible but requires operational ownership. Overall, MLflow fits organizations that want reproducible ML work, clearer governance, and a common language for experiments and model releases across tools and teams.