Semantic Search System Design: Problem Framing and ML Requirements

Every modern application relies on search, but moving from simple keyword matching to understanding user intent requires a structured machine learning approach. This text-based course guides you through the foundational steps of framing semantic search problems and defining precise technical requirements. You will transition from thinking about search as a database query to designing it as a scalable, intelligent system. Through detailed written explanations and architectural walkthroughs, you will learn how to align business goals with machine learning metrics, select the right retrieval strategies, and plan for real-world production constraints. What you'll learn: 1. Understand the core differences between lexical search and embedding-based semantic search. 2. Frame business requirements into clear machine learning objectives and key evaluation metrics. 3. Analyze scale, latency, and storage constraints for high-throughput search applications. 4. Evaluate the role of vector databases and modern retrieval-augmented generation patterns. 5. Design hybrid search architectures that combine dense and sparse retrieval methods. 6. Practice solving system design scenarios through structured written exercises. The course begins with fundamental terminology and concepts of search systems before moving into the step-by-step process of requirement gathering and architectural planning. You will study practical design patterns, learn how to handle trade-offs between accuracy and latency, and review common system design scenarios. This course is designed for software engineers, aspiring machine learning engineers, and technical product managers who want to understand the fundamentals of ML system design. No prior machine learning experience is required, though basic familiarity with software architecture concepts is helpful. Start reading today to master the foundations of semantic search system design.