About DVC (Data Version Control)
An open-source tool for data and model versioning, reproducible pipelines, and experiment management that integrates with Git and external storage backends.
Key Features
- Data & model versioning — track large files and datasets alongside Git by using external remotes.
- Pipeline automation — define reproducible pipeline stages and dependencies.
- Experiment tracking — track experiments, compare results, and reproduce states.
- Integrations & extensibility — works with cloud storage (S3, GCS, Azure), CI, and IDEs.
Use Cases & Best For
Data science teams that want Git-friendly data and model versioning with reproducible pipelines.
Projects that need to manage large datasets and keep artifacts versioned across environments.
About MLOps & Monitoring
Model operations and lifecycle management