Your question is Scaling ML Pipelines 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're putting a machine learning model into production and need the surrounding pipeline to keep up as usage grows. You want a design that can handle larger training data, more frequent retraining, and higher inference demand without becoming hard to operate.
How do you ensure the scalability of a machine learning model in production?