Your question is Model Selection Tradeoffs at Scale. 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 choosing a model for a supervised learning system that will run at large scale in production. Several candidates are on the table, and the team needs a clear way to compare them before rollout.
What trade-offs do you consider when selecting a model for large-scale deployment?