MLflow vs DVC: What’s the DifferenceMLflow tracks experiments and manages the model lifecycle; DVC version-controls data and pipelines like Git for datasets — they solve different problemsData ScienceComparison
DVCGit for data — version control for datasets, models, and ML pipelines without bloating your repoMLOpsVersion Control
MLflowOpen-source platform for managing the ML lifecycle — experiment tracking, model registry, deployment, and reproducibility across any ML frameworkMLOpsExperiment-tracking