DVC is a data versioning tool and pipeline orchestrator designed to track large datasets and machine learning models using external storage and metadata pointers. It integrates with Git by utilizing placeholders to keep heavy artifacts out of the repository while maintaining a versioned link between code and data.
The system manages remote data caches through a synchronization layer that connects local environments to cloud storage or network filesystems. It also functions as an experiment tracker, recording hyperparameters and metrics to compare the performance of different model iterations.
The framework supports the definition of reproducible computational graphs by managing dependencies between code and commands. This capability enables the tracking of model lineage and the validation of data versioning consistency through commit-stage hooks.
