Chapter 5 Notes: Case Studies - Real-world ML Systems

Overview

This chapter presents detailed case studies of ML systems commonly found at major tech companies. Each case study walks through the complete system design process, from requirements gathering to deployment and monitoring.

Case Studies Covered

1. Recommendation System (Netflix, Amazon, YouTube)
2. Search Ranking System (Google, Bing)
3. Feed Ranking System (Facebook, Twitter, Instagram)
4. Fraud Detection System (PayPal, Stripe)
5. Ad Targeting System (Google Ads, Facebook Ads)

Common Interview Questions

• Design a recommendation system for an e-commerce platform
• How would you build a real-time fraud detection system?
• Design a search ranking system for a social media platform
• Build a news feed ranking algorithm

Design Patterns

• Collaborative Filtering: User-based and item-based approaches
• Content-Based Filtering: Feature extraction and similarity computation
• Hybrid Approaches: Combining multiple recommendation strategies
• Real-time vs Batch Processing: When to use each approach

Key Takeaways

• Start with simple solutions and iterate
• Consider both technical and business requirements
• Think about scalability from the beginning
• Plan for monitoring and maintenance

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