Lilly Tech Systems
ML System Design Interview
Master machine learning system design interviews with complete walkthroughs of real-world systems. Learn the structured framework top candidates use, study architecture patterns and trade-offs, and practice with full scoring rubrics used at FAANG companies.
Your Learning Path
Follow these lessons in order to master ML system design interviews, or jump to any design problem you want to practice.
1. Framework for ML System Design
The 4-step approach to any ML system design question: requirements, architecture, deep dives, and trade-offs. Time management and scoring rubrics explained.
2. Design a News Feed Ranking System
Full walkthrough: requirements gathering, candidate generation, ranking model architecture, feature engineering, serving infrastructure, and evaluation metrics.
3. Design Search Autocomplete with ML
Full walkthrough: query prediction models, personalization, real-time serving at scale, trie vs. ML approaches, and evaluation methodology.
4. Design Email Spam Detection
Full walkthrough: feature engineering for text and metadata, model selection trade-offs, real-time scoring pipeline, feedback loops, and adversarial robustness.
5. Design Ride ETA Prediction
Full walkthrough: spatial-temporal feature engineering, graph neural networks for road networks, real-time traffic updates, and accuracy vs. latency trade-offs.
6. Design Ad Click Prediction
Full walkthrough: CTR prediction pipeline, feature interaction modeling, real-time bidding integration, exploration-exploitation strategies, and calibration.
7. Design Content Moderation System
Full walkthrough: multi-modal classification (text, image, video), human review loop design, policy engine architecture, and scaling to billions of posts.
8. Reusable Patterns & Tips
Common architectural patterns across all ML system designs, communication framework for interviews, and an interactive FAQ accordion with expert answers.
What You'll Learn
By the end of this course, you will be able to:
Design End-to-End ML Systems
Architect complete machine learning systems from requirements to deployment, covering data pipelines, model training, serving infrastructure, and monitoring.
Analyze Trade-Offs
Reason about latency vs. accuracy, batch vs. real-time, simple vs. complex models, and make principled decisions that interviewers want to hear.
Define ML Metrics
Choose the right online and offline metrics for each system, design A/B tests, and connect business objectives to model optimization targets.
Communicate Clearly
Structure your 45-minute interview to cover all critical components while going deep where it matters most, using the framework top candidates rely on.