Write down the operational decision.
Describe who uses the prediction and what happens next. For example: a quality reviewer inspects images flagged for a missing component. Record the current workflow so a pilot can compare both detection quality and the review work it creates.
Name the task owner and technical counterpart. Define the image input and the expected output. Identify the mistakes with the greatest operational cost.
Agree on evidence before the experiment.
Choose a representative evaluation set and criteria that reflect the task. For rare defects, overall accuracy is especially misleading. Decide how to measure missed defects, false alerts, and review volume, and record what would make the team iterate or stop.
Set task-specific targets with the people responsible for the outcome. Report results per class and important capture condition. Compare against the current process where a fair comparison is possible.
Bring integration constraints into model selection.
A successful notebook experiment may still be unsuitable for the target environment. Discuss camera access, network availability, response time, hardware, data retention, and who maintains the integration. Confirm requirements early enough to influence the experiment.
Estimate input volume and peak workload. Identify security and procurement reviews needed for a pilot. Document fallback behavior when inputs or predictions are unusable.
Finish with an explicit next step.
Review predictions with domain experts and engineers. Summarize observed limitations, estimated integration work, and unresolved questions. The right outcome may be a production evaluation, another data collection round, a narrower task, or a decision not to proceed.