There is a famous paid course called Grokking the Machine Learning System Design Interview. GitHub is full of open-source summaries and notes derived from this course.
A community-driven repository inspired by Alex Xu’s book. This is where you find multiple solutions to "Design a video recommendation system" from different senior engineers. Compare their trade-offs.
The original book Machine Learning System Design Interview by Alex Xu is a highly regarded, paid resource. However, a significant ecosystem of unofficial GitHub repositories exists, containing summaries, annotated PDFs, solutions to practice problems, and community-driven notes. This review focuses on these GitHub resources, not the official book.
For those preparing for Machine Learning (ML) System Design interviews, GitHub hosts several authoritative repositories that provide comprehensive frameworks, case studies, and PDF guides. These resources are designed to help you transition from academic ML to production-level infrastructure design. Core Study Guides & Frameworks
Machine Learning Interviews (alirezadir): Features a 9-Step ML System Design Formula . It provides a rigorous template covering everything from clarifying business goals to scaling features and assessing data availability .
ML Systems Design (chiphuyen): An open-source project by Chip Huyen that offers a "Machine Learning System Design Draft PDF" . It includes 27 open-ended interview questions and a structured look at the data pipeline, modeling, and serving stages .
Machine Learning Study Guide (smhosein): A centralized hub that links to various ML System Design templates, blog resources from major tech companies, and direct PDF overviews of interview themes . Popular Interview Templates
Most successful candidates use a standard flow to answer open-ended design questions :
Project Setup: Clarifying requirements, business goals, and performance constraints .
Data Pipeline: Addressing data availability, feature engineering (e.g., one-hot encoding, feature scaling), and handling imbalanced classes .
Modeling: Selecting algorithms, training, and offline evaluation .
Serving & Infrastructure: Designing for low latency, scalability, and online monitoring . ml-system-design.md - Machine-Learning-Interviews - GitHub
Create a single-page PDF cheat sheet based on the best elements from all GitHub repos. Include:
While not ML specific, this repo contains process diagrams. For ML interviews, you steal their diagram formats (Load balancers -> API Gateway -> Feature Store).
While not a direct PDF, this repo indexes the best video breakdowns of ML systems. Videos are better than PDFs for understanding the motion of data through a pipeline.
While not strictly an interview book, Chip Huyen’s O'Reilly book is the bible for production ML. Interviewers often borrow concepts from Chapter 4 (Training Data) and Chapter 8 (Monitoring).
