A model can pass an offline evaluation while its surrounding service remains unready. Review the entire prediction path: input validation, feature availability, authorization, model execution, downstream effects and response delivery. Each stage needs an observable failure policy.
Version the artifacts that define behavior. For ML this includes preprocessing and model weights; for an AI application it may also include prompts, tool schemas, retrieval configuration and source versions. A rollback needs compatible artifacts, not just an old model name.
Test operating constraints with a representative workload. Inspect latency percentiles, concurrency, queue growth, timeouts and resource usage. Measure quality after any optimization that changes batching, precision, context or routing. A reduction in average cost can conceal a quality regression in a valuable slice.
Define what monitoring can establish immediately and what requires delayed outcomes. Invalid inputs and dependency errors are visible quickly; accuracy may require labels collected days later. Distribution change is a reason to investigate, not proof that the model is wrong.
Finish with explicit release and rollback criteria. State who or what evaluates them, where evidence is recorded, and how pending conclusions are communicated. This turns a vague claim of readiness into a reviewable operating decision.