DVC is a free, open-source Data Version Control tool that manages and versions datasets, models, and large files to create reproducible ML workflows.

Key Features:

• Version control for images, audio, video, and text with git-like commands
• Storage-agnostic remote cache support (e.g., S3) for large file management
• Workflow reproducibility with commands to track data, models, and experiments

Use Cases:

• Tracking datasets and model artifacts across ML experiments
• Collaborating on large-data projects using shared remote storage
• Reproducing and auditing ML training pipelines and results

Benefits:

• Reproducible, auditable ML workflows for teams of any size
• Scales storage and versioning without bloating git repositories
• Open-source and widely adopted across startups to Fortune 500 companies