Should you use real-time inference (low latency, high cost) or pre-computed batch inference?
How do you handle streaming data (Kafka/Flink) versus batch processing (Spark)? 3. Model Selection and Training This is where you demonstrate your technical depth.
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Explain how you would run an A/B test . What is the control group? How do you measure statistical significance? 5. Deployment and Scaling An ML system must live in production.
The secret to passing the ML system design interview is . Don't just lecture; treat the interviewer as a teammate. Propose a solution, explain the trade-offs, and ask for their feedback on specific constraints. Model Selection and Training This is where you
Where does the data come from? (User logs, relational databases, third-party APIs).
Latency requirements (online vs. offline), data privacy (GDPR), and throughput. What is the control group
Discuss categorical vs. numerical features, embeddings, and how to handle missing values.