lineage

The trap is describing plain software CI/CD. The signal is the three things ML adds: data validation, a model-quality gate against a baseline, and versioning data plus model plus code together. Here is what is genuinely different.

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

When a model regresses or an auditor asks 'how was this built,' guesswork is a failing answer. The signal is tracking the whole chain so any model is reproducible and any past decision is explainable. Here is the chain.

'Why was I denied?' is a question regulators and customers will ask, and 'we retrained since then' is not an acceptable answer. Exact reproduction means versioning everything. Here is what 'everything' actually means.

Promotion is the easy part. Governance is who is allowed to 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' is the wrong answer. Reproducibility means pinning the environment, the data, the code, and the hardware-level nondeterminism, all at once. Here is the full checklist and where it leaks.

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