Model selection is often framed as a contest for the highest score. Production changes the question. A model must fit latency, memory, interpretability, data availability, maintenance capacity, and the cost of a wrong decision. A small, stable model can be more valuable than a marginally more accurate one that is expensive to serve or impossible to explain.
Evaluation should mirror the decision environment. Choose metrics that reflect the cost of false positives and false negatives, examine performance across meaningful slices, and test the model against realistic temporal and operational splits. A single aggregate score can hide failure exactly where the business is most exposed.
The model registry as a control point
A registry is more than a shelf for artifacts. It records lineage, version, training data, evaluation results, approval status, and intended use. That record lets a team answer basic operational questions quickly: Which model is live? What changed? Who approved it? Can we reproduce it? What should replace it if it fails?
Interpretability completes the handoff. Explanations should help a decision owner understand behavior, investigate anomalies, and challenge a prediction when context demands it. They do not turn an imperfect model into a perfect authority. They make the model's limits visible enough for responsible use.
Production questions
- Which metric maps to the actual business decision?
- What slices or populations require separate evaluation?
- Can a new team member reproduce the candidate model?
- What evidence is required before promotion to production?