Feed Ranking System Design: Building Scalable Recommendation Engines

How do major platforms decide what content to show you in real time? Designing a feed ranking system requires balancing massive scale, low latency, and highly personalized machine learning models. This text-only course guides you through the foundational concepts of feed ranking system architecture. You will gain a clear understanding of how data flows from user interactions to real-time recommendations, preparing you to design robust, scalable machine learning systems. What you'll learn: 1. Understand the core architecture of personalized feed ranking and recommendation systems. 2. Explore modern feature stores and data pipelines designed for low-latency retrieval. 3. Analyze candidate generation and ranking stages, including modern vector-based search. 4. Evaluate scalable inference strategies to serve model predictions to millions of users. 5. Apply foundational MLOps principles to monitor system health and prevent model drift. 6. Practice designing end-to-end system architectures through structured written exercises. The course begins with essential terminology, basic system components, and architectural definitions before diving into data pipelines, ranking algorithms, and scalability challenges. You will progress from raw data concepts to fully realized system designs through detailed text explanations and practical design scenarios. This course is designed for software engineers, aspiring data scientists, and system design beginners who want to understand the infrastructure behind modern recommendation engines. No prior experience with complex machine learning systems is required. Start learning how to design scalable feed ranking systems today.