Your question is Real-Time ML Data Pipeline Design. 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).
How would you design a data pipeline that supports real-time machine learning in a production environment?
Explain how events become features and predictions with low latency, while also supporting offline training and delayed labels. Discuss architecture, data contracts, serving, monitoring, failure recovery, and how you would prevent training-serving skew.
