Your question is Evaluating Model Robustness in Production. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You've built a machine learning model that looks good in offline testing, and your team wants confidence that it will hold up when data and usage patterns change. You need a practical evaluation approach that goes beyond a single validation score.
How do you ensure that your machine learning models are robust?