Your question is Tune Production Model Hyperparameters. 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 are tuning a supervised learning model that will be deployed in production. Several candidate settings improve validation score, but the team needs a repeatable way to pick hyperparameters without overfitting to the holdout set.
How would you tune hyperparameters for a production machine learning model?