Data Version Control
Data Version Control (DVC) is an open-source version control system for machine learning projects that manages and tracks datasets, models, and experiments. It integrates with Git to version large files and directories, storing them in remote storage like S3, GCS, or Azure Blob Storage while keeping lightweight metadata in Git. DVC enables reproducible machine learning workflows by capturing dependencies, parameters, and metrics.
Developers should learn DVC when working on machine learning or data science projects that require tracking changes to datasets, models, and experiments over time. It is essential for ensuring reproducibility, collaboration, and efficient management of large files in ML pipelines, particularly in team environments or production settings where model versioning and data lineage are critical.