ML System Design Framework - Interactive Visualization

ML system design interviews test whether you can define requirements, estimate scale, architect a data and serving pipeline, deep-dive into critical components, and articulate tradeoffs clearly. This interactive framework guides you through all five dimensions with example content for four canonical ML systems: fraud detection, recommendation, search, and LLM products. The 35-minute structure matches real interview expectations at top ML engineering teams.

• Requirements: functional (the ML task), non-functional (latency SLA, scale), and constraints (data, labels, imbalance)
• Scale estimation: derive QPS from DAU × requests/user ÷ 86400, then multiply by 3–5× for peak
• High-level design: data pipeline, feature store, model serving, and feedback loop - all four must be addressed
• Deep dive: pick the hardest component (often the feature store or serving layer) and go deep on trade-offs
• Interview mode shows time allocation per section - 5 min requirements, 10 min deep dive, 5 min tradeoffs

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.