Version Control And Reproducibility

via “git-integrated experiment branching and reproducibility”

Git for data and ML — version large files, experiment tracking, pipeline DAGs, remote storage.

Unique: Stores experiments as Git commits with full code and parameter snapshots, enabling perfect reproducibility without external databases. The experiment registry maps Git commits to experiment metadata, making experiments shareable and auditable via Git history.

vs others: More reproducible than MLflow because all inputs are captured in Git, but less convenient than cloud-based platforms because experiments are stored locally and require Git operations.