DVC (Data Version Control)

Technical Use Cases

DVC is purpose-built for the MLOps lifecycle. Common technical use cases include:

• Reproducible ML pipelines – Define and version data pipelines as code, ensuring any experiment can be exactly reproduced by any team member at any point in time using Git-compatible metadata.
• Dataset versioning at scale – Version petabyte-scale datasets without bloating Git repositories, using DVC's remote storage abstraction layer.
• Experiment tracking & comparison – Log hyperparameters, metrics, and artifacts per experiment run; diff and compare runs directly from the CLI or VS Code extension.
• End-to-end MLOps lineage – Maintain full traceability from raw data ingestion through model training to deployment artifacts, supporting audit and governance requirements.
• CI/CD for ML – Integrate DVC into Git-based CI/CD pipelines to automate data validation, model retraining, and artifact promotion.
• Multi-tool MLOps stacks – Connect DVC with MLflow for experiment metadata, Amazon SageMaker AI for training infrastructure, and VS Code for local development — all anchored to Git as the source of truth.