lineage

The trap is describing ordinary software CI/CD. The signal is the three additions ML brings: data validation, a model-quality gate against a baseline, and versioning data plus model plus code as a unit. Here is what truly differs.

Reproducibility is what divides an ML platform from a heap of notebooks. The signal is capturing the full provenance (data, code, config, metrics) and a registry that governs promotion. Here is what to track and why each piece counts.

When a model regresses or an auditor asks 'how was this built,' guessing is a failing answer. What matters is tracking the entire chain so any model can be reproduced and any past decision explained. Here is the chain.

Regulators and customers will ask 'Why was I denied?', and 'we retrained since then' will not satisfy them. Reproducing a prediction exactly requires versioning everything. This spells out what 'everything' really covers.

Promotion is the easy part. Governance covers who may ship what, on whose sign-off, with what documented, and how a model gets retired. Here is the control plane an auditor actually asks for.

'Just set a seed' misses the point. Reproducibility means pinning the environment, the data, the code, and the hardware-level nondeterminism together. Here is the full checklist and where it springs leaks.

You cannot put a 2 TB dataset in git, and copying it per experiment bankrupts you. What matters is knowing how content-addressed versioning works and when file-level (DVC) versus branch-level (lakeFS) fits. Here is the decision.