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ML System Design: 6-Step Framework

The definitive framework for ML system design interviews. Covers all 6 steps with exact timing, what interviewers look for at each step, and how to stand out from other candidates.

40 min read 2 sections 1 interview questions
MLSD FrameworkML System Design InterviewProblem FramingFeature EngineeringModel SelectionEvaluation MethodologyProduction MLServing PipelineMonitoringRetraining StrategyML PipelineFeature Store

What Makes ML System Design Different

Traditional system design interviews test your knowledge of distributed systems. ML system design interviews test that PLUS your understanding of the ML lifecycle: data pipelines, feature engineering, training infrastructure, model evaluation, and production monitoring (Chip Huyen, Designing ML Systems: https://www.oreilly.com/library/view/designing-machine-learning/9781098107956/). The key difference: ML systems fail silently. A model that degrades due to drift still serves predictions — you won't get a 500 error. You need to design for measurement and observability from the start.

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