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Discuss ML infrastructure fundamentals

DoorDashMachine Learning Engineer · Technical Screen
HardMachine Learning EngineerTechnical ScreenML System Design

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Discuss ML infrastructure fundamentals evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Question

What are the key components of a modern machine-learning infrastructure stack and how do they interact? Describe how you would design a scalable feature store to support both offline training and real-time inference. Explain strategies to ensure reproducibility and versioning of data, code, and models in an ML pipeline. How would you monitor and troubleshoot production ML services for latency, drift, and model degradation?