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Browse 3 real-world technical and behavioral interview questions about Model registry. Review scenarios, edge cases, and architectural best practices.
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.
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.
Features must be computable at decision time, training and serving must share one transformation implementation, the model and dataset version must be pinned together, drift and realised performance monitored separately because labels arrive late, and rollback must move model and features as one unit.