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How do you approach system design for ML interviews?
Strong answers clarify metrics and constraints first, then walk the lifecycle: data → features → model → serving → feedback—always naming failure modes and monitoring.
DEFINITION
ML system design interviews test whether you can turn ambiguous product goals into a reliable machine learning system—covering data, training, serving, evaluation, and operations under explicit constraints.
A repeatable outline
1) Goals and online/offline metrics. 2) Constraints (latency, cost, privacy). 3) Data sources and labels. 4) Training/eval loops. 5) Inference path and fallbacks. 6) Monitoring, drift, and rollout (shadow, canary). 7) Trade-offs you rejected.
Common interviewer probes
Cold start, class imbalance, leakage, p99 latency, cost per prediction, human-in-the-loop, and how you detect silent failures when accuracy looks fine offline.
FAQ
Should I draw transformers every time?
Only if the problem needs them. Interviewers prefer correct simpler models with solid data and serving design over buzzword architectures.
Expert
Rajeev Chandran — Founder & Curriculum Architect, Deepskilling. Designs practice-first AI, cloud, and systems programmes—from RAG and GPU labs to executive GenAI decision frameworks.