MLflow
Open-source platform for managing the ML lifecycle — experiment tracking, model registry, deployment, and reproducibility across any ML framework
Visit MLflow →MLflow tracks the parts of ML development that get messy without tooling — which hyperparameters produced which model, comparing runs against each other, and a model registry for versioning what’s actually deployed. It’s framework-agnostic, working the same way whether the underlying model is PyTorch, TensorFlow, or scikit-learn.
Worth adopting once you’re running enough experiments that “which run produced this result” stops being answerable from memory or a spreadsheet — a common threshold most real ML projects hit fairly quickly.