This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design an End-to-End ML System states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
##### Question
Design an end-to-end machine-learning system for a real-time recommendation product. Explain data collection, feature engineering, model training, online serving, monitoring, and scalability considerations.
Overview: This interview question evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer for Design an End-to-End ML System states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
hardSoftware EngineerTechnical ScreenML System Design
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Design an End-to-End ML System
System Design: Real-Time Recommendation ML System
Context
You are tasked with designing an end-to-end machine-learning system that serves real-time recommendations in a consumer-facing product (e.g., feed, products, videos). The system must handle high read traffic and evolving content and user behavior.
Assumptions (you may refine during the interview):
Traffic: ~10k QPS; p95 latency target ≤ 150 ms for recommendation API
Inventory: 10M items; daily new/expiring items
Feedback: clicks, likes, purchases; implicit and explicit signals
Privacy: user consent, PII minimization, right-to-erasure compliance
Requirements
Explain and justify the design for each of the following:
Data collection and event pipeline
Feature engineering and feature store (offline and online)