Ask about the number of active users, queries per second (QPS), data volume, and maximum allowable latency (e.g., responses must be under 50ms). 2. Data Engineering and Feature Pipeline An ML model is only as good as the data feeding it.
To explore the topic further, you can find the book notes and related materials in GitHub repositories dedicated to system design interviews . Follow-Up: How Can I Help You Further? If you're studying for these interviews, I can help by:
An ML system's lifecycle doesn't end at deployment. Models degrade over time. Ask about the number of active users, queries
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(e.g., Latency, budget, data privacy). 2. Data Engineering & Features Data is the lifeblood of any ML system. Data Sources: Where does the data come from? Labeling: Is it supervised? How do we label? To explore the topic further, you can find
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A key skill Aminian's book teaches is evaluating trade-offs. An interviewer cares less about finding the "perfect" design and more about your ability to articulate the pros and cons of different architectural decisions. According to the book's publisher notes, the primary goal is to assess how you navigate choices like: Models degrade over time
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Used to measure business impact in production. (e.g., Click-Through Rate, Conversion Rate, Revenue, User Retention). 6. Deployment and Serving Strategy Explain how the model will interact with the real world.
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