Description
Machine-learning experiments, models, metrics, artifacts, and deployments can be tracked through an ML lifecycle platform. ML engineers use MLflow for experiment tracking, model registry, packaging, and reproducible workflows. Artifacts can include datasets, secrets, model weights, and production metadata.