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.

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

System Design: Real-Time Recommendation ML System

Context

Requirements

Solution

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Design an End-to-End ML System

Overview

System Design: Real-Time Recommendation ML System

Context

Requirements

Solution

Comments (0)