Building Efficient Retrieval and Ranking Pipelines for ML Systems

Modern recommendation engines and search systems must process millions of potential items in milliseconds. Understanding how to structure multi-stage retrieval and ranking pipelines is essential for building scalable machine learning systems. This text-focused course guides you through the architecture of modern retrieval systems, helping you transition from basic search concepts to designing production-ready pipelines that balance accuracy, computational cost, and speed. You will learn to: 1. Understand the foundational concepts of multi-stage retrieval and ranking architectures. 2. Explore candidate generation techniques using vector databases and approximate nearest neighbor search. 3. Apply feature engineering and ranking models to score and filter candidates efficiently. 4. Implement modern retrieval-augmented generation (RAG) patterns for context-aware systems. 5. Optimize system latency using caching, quantization, and model distillation strategies. 6. Evaluate pipeline performance using offline metrics and online testing methodologies. Starting with core terminology and system constraints, the course walks you through candidate generation, deep ranking, and final selection stages. This course is designed for software engineers, aspiring data scientists, and machine learning beginners who want to understand large-scale system design, with no advanced prerequisites required. Start reading today to master the core architecture behind modern search and recommendation systems.