Machine Learning System Design Interview: Frameworks and Casebook
Advanced27 hours16 sections102 lessonsRated 4.9 out of 5 from 29 ratings
Design machine learning systems that connect product decisions to labels, features, training, serving, experiments, monitoring, and recovery, then apply the method to realistic interview cases.
What you will be able to do
- Turn product goals into labels, evaluation criteria, and capacity estimates.
- Design data and feature pipelines that avoid leakage and training-serving skew.
- Plan experiments, controlled releases, rollback, and monitoring for production models.
- Apply one design method to recommendation, ads, search, moderation, fraud, and RAG cases.
Who this course is for
ML engineers preparing for machine learning system design rounds at the mid-level and senior level.
Helpful to know first
- Working knowledge of ML modeling and evaluation, plus basic system design concepts.
Course content
16 sections 102 lessons 27 hours
Practice a structured ML design interview: clarify the task, manage time, state assumptions, and explain decisions with concrete trade-offs.
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Rated 4.9 out of 5 from 29 ratings
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