Designing a Twitter-Style Feed System with ML Relevance Ranking

Building a modern feed that keeps millions of users engaged requires more than just database queries; it demands a robust machine learning system. This text-based course guides you through the foundational concepts of designing a highly scalable, ML-driven recommendation feed. You will transition from understanding basic system architecture to designing complex, multi-stage ranking pipelines. By studying core components like candidate generation, feature engineering, and real-time inference, you will learn how to design systems that deliver relevant content to users in milliseconds. What you will learn: Understand the foundational architecture of feed retrieval and ranking systems; Define and scope system requirements for millions of daily active users; Design a two-stage recommendation pipeline featuring candidate generation and heavy ranking; Apply modern feature engineering techniques to predict user engagement and relevance; Explore evaluation strategies, including offline metrics and online A/B testing methodologies; Configure scalable data pipelines for real-time feature updates and model inference. The course starts with essential terminology and system design fundamentals before diving into the mechanics of ML ranking models and high-throughput data pipelines. Through clear written explanations, practical architecture walkthroughs, and design exercises, you will build a solid blueprint for scalable feed recommendation engines. This course is designed for beginner software engineers, aspiring system designers, and data scientists looking to understand the system architecture side of machine learning; no prior experience with complex system design is required. Start reading today to master the fundamentals of large-scale ML system design.