DVC enables data scientists and ML engineers to version datasets and trained models by storing them in cloud storage while keeping metadata in Git. It provides pipeline functionality so that only affected steps rerun when code or parameters change. Experiment tracking allows comparing runs across data, code, parameters, metrics, and visualizations—all locally without requiring a dedicated server.