model versioning

AI/ML on Linux can devolve into chaos as data, models, and experiments sprawl; this guide delivers a Git‑native, Bash‑first branching playbook: keep main releasable with short‑lived feat/ exp/ datafix/ model/ branches, pair Git LFS with DVC for data/model versioning, use DVC pipelines with params and tags for reproducibility, add pre‑commit guardrails, plan hotfixes/rollbacks, and bootstrap fast with quick installs and a minimal repo template.

Practical, Bash-first playbook for end-to-end AI model management on Linux: version code/data/models with Git LFS + DVC, track experiments in MLflow, lock dependencies and containerize for consistent deploys, register/serve via MLflow or Hugging Face, and automate CI promotion gates—enabling reproducible, auditable pipelines that prevent regressions and speed releases.