Promotion from staging to production, champion and challenger, automated gates on evaluation, rollback of a model, and shipping a model like code.
3 questions in MLOps & ML Platform.
It is the system of record for which artefact is authoritative and why. It binds each version to the data snapshot, code commit and evaluation that produced it, records stage transitions and who approved them, and gives serving a stable identifier so any past prediction can be traced to the exact model that made it.
Gate on absolute thresholds, on a comparison against the incumbent evaluated on identical data, on per-slice regression checks so an aggregate gain cannot hide a subgroup loss, and on operational limits like latency and artefact size. Keep a human approval where a wrong decision is legally consequential.
Re-pointing traffic at the previous version is only safe if nothing else moved with it. The feature transformations, the input schema and any consumer calibrated to the current score distribution may have changed, so the unit of rollback is the model plus its features and its contract.