Design an End-to-End ML System

Q: Design an End-to-End ML System

This question evaluates a candidate's competency in designing end-to-end machine learning systems for real-time recommendation services within the ML System Design domain, covering data collection and event pipelines, feature engineering and stores, model training and retraining, online serving architecture, monitoring, and operational constraints.

Q: How do I approach ML System Design interview questions?

ML System Design questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master ml system design interviews.

Q: What difficulty level is this interview question?

This is a hard difficulty ML System Design question, commonly asked during Technical Screen rounds at OpenAI.

Q: What role is this question designed for?

This question is commonly asked for Software Engineer candidates at OpenAI during technical interviews.

Question

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)
Model training, labeling, and retraining strategy
Online serving architecture (candidate generation, ranking, re-ranking)
Monitoring, alerting, and experimentation
Scalability, reliability, and cost considerations

Design an End-to-End ML System

Quick Overview

System Design: Real-Time Recommendation ML System

Context

Requirements

Solution

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