Scenario

You've trained and shipped a model, and the team wants confidence that its performance will hold up outside offline experiments. You need a clear evaluation approach that catches weak generalization, unstable predictions, and bad decision thresholds before the model causes downstream issues.

Question

How do you ensure that your machine learning models are robust and reliable?