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Machine Learning System Design

Name: Machine Learning System Design
Price: 59 USD
Rating: 4.44 (403 reviews)
Author: Khang Pham

Gain insights into ML system design, state-of-the-art techniques, and best practices for scalable production. Learn from top researchers and stand out in your next ML interview.

4.4

21 lessons

1 AI mock interview

Created by Khang Pham

What's included

21 lessons across 7 modules
6 quizzes with instant feedback
1 AI mock interview with scoring rubrics
AI Tutor on every lesson

Join 3.1 million developers at

What You'll Learn

Design end-to-end ML systems for production environments: feature engineering, training pipelines, inference, and metrics evaluation
Build a video recommendation system with candidate generation and ranking models
Optimize feed ranking with personalized models for user engagement
Design ad click prediction models and recommender system architectures
Architect Airbnb-style rental search ranking with booking prediction models
Estimate food delivery times using scalable ML system design principles

Learning Roadmap

21 Lessons6 Quizzes2 Assessments

Machine Learning Primer

Get familiar with core machine learning principles, from feature engineering to model deployment.

Introduction

Feature Selection and Feature Engineering

Training Pipeline

Inference

Metrics Evaluation

Video Recommendation

Discover the logic behind developing and optimizing scalable video recommendation systems for enhanced user engagement.

Problem Statement and Metrics

Candidate Generation and Ranking Model

Video Recommendation System Design

Feed Ranking

3 Lessons

Work your way through optimizing feed ranking with personalized models for enhanced user engagement.

Ad Click Prediction

3 Lessons

Enhance your skills in designing and optimizing ad click prediction models for better ad performance.

Ad Recommender System Design

Mock Interview

Premium

Rental Search Ranking

3 Lessons

Take a closer look at designing Airbnb's rental search ranking system with a booking prediction model and performance metrics.

Estimate Food Delivery Time

3 Lessons

See how it works to design an accurate, scalable food delivery time estimation system.

Conclusion

Build on comprehensive insights into designing practical Machine Learning systems for diverse applications.

Summary

Machine Learning Knowledge

Assessment

Machine Learning Model Diagnosis

Assessment

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

About this course

WHY I WROTE THIS COURSE

ML System Design interviews reward candidates who can walk through the full lifecycle of a production ML system, from problem framing and feature engineering through training, inference, and metrics evaluation. This course covers that lifecycle through five real-world systems that reflect the kinds of problems asked at companies like Meta, Snapchat, LinkedIn, and Airbnb. You'll start with a primer on core ML system design concepts: feature selection and engineering, training pipelines, inference architecture, and how to evaluate models with the right metrics. Then you'll apply those concepts to increasingly complex systems, including video recommendation, feed ranking, ad click prediction, rental search ranking, and food delivery time estimation. Each system follows a consistent structure: define the problem, choose metrics, design the architecture, and discuss tradeoffs. The course draws directly from hundreds of recent research and industry papers, so the techniques you'll learn reflect how ML systems are actually built at scale today. It is designed to be dense and efficient, ideal if you have an ML System Design interview approaching and want to go deep on production-level thinking quickly. Learners from this course have gone on to receive offers from companies including Snapchat, Meta, Coupang, StitchFix, and LinkedIn.

ABOUT THE AUTHOR

Khang Pham

I built many online Machine Learning systems that serve 100k RPS. I have 6 years of experience as Machine Learning Engineer. I had offers from Google, Snapchat, Coupang etc. I'm owner of the popular ML interview repo at http://mlengineer.io/

Learn more about Khang

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Anthony Walker

@_webarchitect_

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ML Engineer

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