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Grokking the Machine Learning System Design Interview
Gain insights into designing robust machine learning systems. Delve into key concepts, methodologies, and best practices to build efficient, scalable, and reliable ML solutions.
4.7
97 lessons
17h
Created by Fahim ul Haq
What's included
- 97 lessons across 16 modules
- 97 quizzes with instant feedback
- AI Tutor on every lesson
Why this course
- Ace ML System Design InterviewsNavigate complex ML system design interviews using a structured 6-step framework that showcases your design fluency.
- Architect Scalable ML SystemsDesign and deploy scalable ML systems that meet business and technical requirements, ensuring robust performance in production.
- Evaluate Model Performance EffectivelyImplement and interpret evaluation metrics to assess model performance, ensuring alignment with business objectives.
- Communicate Design Decisions ConfidentlyArticulate your design choices and trade-offs clearly, demonstrating technical leadership and strategic thinking in discussions.
Who wrote it
Fahim ul Haq
Software Engineer, Distributed Storage at Meta and Microsoft, Educative (Co-founder & CEO)
Fahim ul Haq is the co-founder and CEO of Educative, a hands-on learning platform for software engineers. He spent nearly a decade as a software engineer at Microsoft and Meta, where he gained deep experience building large-scale distributed systems. His work spanned Project Red Dog at Microsoft, which evolved into Microsoft Azure, and Scuba, Meta's real-time data analytics engine. In 2015, Fahim co-founded Educative to help developers get hands-on with new skills. Fahim's courses have helped hundreds of thousands of engineers learn System Design and prepare for technical interviews. Fahim earned his M.S. in Computer Science from the University of Washington in 2015.
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What You'll Learn
- Master the 6-step ML system design framework for scalable, production-ready machine learning systems.
- Formulate and clarify problems to align machine learning solutions with business objectives and constraints.
- Design and evaluate model architectures suitable for various machine learning tasks and requirements.
- Implement effective data strategies and feature engineering techniques to enhance model performance.
- Utilize evaluation metrics and A/B testing to assess model effectiveness and ensure alignment with business goals.
- Communicate design decisions clearly and effectively during ML system design interviews.
Learning Roadmap
1.
The Interview Framework and Communication
The Interview Framework and Communication
Master the ML system design interview process, focusing on frameworks, time management, and effective communication.
2.
Problem Formulation and Requirements
Problem Formulation and Requirements
Master effective problem formulation, clarifying questions, and metric alignment in ML system design.
3.
Data Strategy: Collection, Pipelines, and Features
Data Strategy: Collection, Pipelines, and Features
9 Lessons
9 Lessons
Master data strategies, tackle cold start issues, and ensure compliance in machine learning systems.
4.
Model Design and Architecture Selection
Model Design and Architecture Selection
9 Lessons
9 Lessons
Master model selection and architectures for efficient machine learning system design.
5.
Evaluation: Offline, Online, and Fairness
Evaluation: Offline, Online, and Fairness
8 Lessons
8 Lessons
Master evaluation techniques for machine learning, focusing on metrics, testing, and fairness.
6.
Serving, Deployment, and MLOps
Serving, Deployment, and MLOps
8 Lessons
8 Lessons
Explore strategies for optimizing machine learning systems, focusing on deployment, performance, and continual learning.
7.
Case Study: Video Recommendation System
Case Study: Video Recommendation System
5 Lessons
5 Lessons
Design an efficient video recommendation system focusing on problem framing, data strategy, architecture, ranking, and evaluation.
8.
Case Study: Social Feed Ranking System
Case Study: Social Feed Ranking System
5 Lessons
5 Lessons
Master social feed ranking through effective strategies, model architecture, and evaluation techniques.
9.
Case Study: Ad Click-Through Rate Prediction System
Case Study: Ad Click-Through Rate Prediction System
5 Lessons
5 Lessons
Master ad CTR prediction through effective strategies in data, model architecture, evaluation, and serving.
10.
Case Study: Semantic Search Engine
Case Study: Semantic Search Engine
5 Lessons
5 Lessons
Master semantic search design by focusing on problem framing, data strategy, model architecture, evaluation, and serving trade-offs.
11.
Case Study: Content Moderation System
Case Study: Content Moderation System
5 Lessons
5 Lessons
Master effective content moderation through robust strategies, architecture, and evaluation methods.
12.
Case Study: Object Detection System
Case Study: Object Detection System
5 Lessons
5 Lessons
Designing effective object detection systems for autonomous vehicles emphasizes real-time performance, safety, and robust data strategies.
13.
Case Study: Visual Search System
Case Study: Visual Search System
5 Lessons
5 Lessons
Master visual search system design through effective problem framing, data strategy, and evaluation metrics.
14.
Case Study: Fraud Detection System
Case Study: Fraud Detection System
5 Lessons
5 Lessons
Master fraud detection through effective data strategies, model architecture, and continuous monitoring.
15.
Case Study: RAG-Based Enterprise Knowledge Assistant
Case Study: RAG-Based Enterprise Knowledge Assistant
5 Lessons
5 Lessons
Master the design and evaluation of enterprise RAG systems for optimal performance.
16.
Case Study: LLM-Powered Code Generation Tool
Case Study: LLM-Powered Code Generation Tool
5 Lessons
5 Lessons
Master AI-driven code generation systems, focusing on performance, privacy, and user interaction.
Certificate of Completion
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About this course
WHY I WROTE THIS COURSE
Machine learning is changing what companies expect from senior engineers. Building a model is only one part of the job. Senior engineers are expected to design ML systems that scale in production while accounting for data quality, infrastructure, latency, reliability, cost, and business requirements. ML system design is a core skill for senior AI and machine learning roles.
I created this course based on my experience designing large-scale systems at Microsoft and Meta, where I worked on infrastructure and real-time analytics, and interviewed hundreds of candidates. The biggest pattern I saw was that even strong engineers struggled to structure ambiguous ML system design problems and communicate trade-offs clearly. This course introduces the 6-step framework I developed to solve that gap.
In this course, you'll master a 6-step ML system design framework that covers everything from problem formulation and requirements gathering to data strategy, model architecture, evaluation, and production deployment. You'll work through real-world case studies spanning recommendation systems, fraud detection, semantic search, content moderation, and LLM-powered applications, tackling the exact challenges faced in interviews. If you're serious about mastering ML system design interviews, this is the best place to start.
ABOUT THE AUTHOR
Fahim ul Haq
Software Engineer, Distributed Storage at Meta and Microsoft, Educative (Co-founder & CEO)
Trusted by 3.1 million developers working at companies
A
Anthony Walker
@_webarchitect_
E
Evan Dunbar
ML Engineer
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Software Developer
Carlos Matias La Borde
S
Souvik Kundu
Front-end Developer
V
Vinay Krishnaiah
Software Developer
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