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ML System Design
How to reason through search, ranking, fraud detection, recommendations, and LLM-powered products.
Worked system designs with the tradeoffs made explicit: latency versus quality, offline versus online, cost versus accuracy. Written the way a strong candidate answers the ML system design round, and the way a senior engineer writes the design doc.
6 posts, from beginner to advanced. Each ends with what to read next.
Designing a Search and Ranking System
Search is one of the highest-leverage ML problems. A well-designed ranking system doubles engagement; a poor one loses users in seconds. Walk through the full architecture from query to result.
Designing a Fraud Detection System
Fraud detection is one of the hardest ML system design problems: extreme class imbalance, adversarial inputs, real-time constraints, and the cost of false positives. Here is how to approach it.
Designing a Real-Time Recommendation System
A recommendation system that feels real-time but stays fast at scale requires careful architecture. Walk through the full design: candidate generation, ranking, serving, and feedback loops.
ML System Design for LLM-Powered Customer Support
Replacing a rules-based support bot with an LLM-powered assistant sounds simple. The production system is not. Walk through the full design: RAG, guardrails, escalation, and cost control.
Latency vs. Quality Tradeoffs in ML Systems
Every ML system lives in a latency budget. Understanding where to spend that budget - and which quality-latency tradeoffs are worth making - is core to ML system design.
ML System Design Interview Framework
ML system design interviews test whether you can translate business requirements into an ML architecture. Here is a structured framework for approaching any ML system design question.