ML system design
| # | File | Covers |
|---|---|---|
| 01 | 01_ml_design_framework.md | the structure to apply to any question; capacity arithmetic; feedback loops |
| 02 | 02_design_a_rag_assistant.md | the most likely AI design question for a backend engineer |
| 03 | 03_design_fraud_detection.md | the canonical classical-ML design: imbalance, cost thresholds, label delay, adversarial drift |
The two things that carry most questions
Say the outline before diving in. Clarify the decision, frame the ML problem, state the baseline, define metrics, then data, model, serving, rollout, monitoring, failure modes. Candidates who start at model architecture have skipped everything that matters.
Retrieve-then-rank covers search, recommendations, ads and RAG. The capacity arithmetic forces it: scoring every candidate with an expensive model at production QPS is impossible, so a cheap high-recall stage feeds an expensive high-precision one.