Lock in dataset versions to ensure your model training results can be reproduced exactly, every time you run experiments.

Test different dataset versions and track their impact on model performance without losing your baseline datasets.

Quickly revert to previous dataset versions if new annotations or data changes negatively impact your model accuracy.

Multiple team members can work on different dataset branches simultaneously without conflicts or data overwrites.

Track exactly which dataset version was used to train each model for full audit trails and compliance requirements.

Monitor how dataset improvements over versions correlate with model performance gains across training runs.

Create branches for different dataset experiments while maintaining a clean main dataset version for production training.

Maintain detailed records of dataset changes and model training history for regulatory compliance and quality assurance.