You have trained a model and the team wants to ship it to production. Before launch, you need a clear validation process that checks whether offline performance is trustworthy and whether the model is ready for real decisions.
How would you validate a model before deploying it into a production environment?
Cross-validation to estimate generalization stabilityCalibration of predicted probabilitiesThreshold tuning for operational useConfusion matrix tradeoffs before launch