Your question is Scaling ML Pipelines. 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 working on machine learning pipelines that need to support larger datasets, more frequent retraining, and more downstream consumers. Before changing the implementation, you want to think through how the pipeline itself should scale as volume and complexity increase.
How do you approach scaling machine learning models?