AI’nt That Easy #29: Systematic Approach to Solving ML System Design Problems: Advanced Generative AI Systems

In building an end-to-end machine learning system, the journey often begins with selecting the right database, tailored to the data type and desired outcomes. While this is a critical foundation, many get stuck at this stage, over-focusing on initial decisions while neglecting the broader challenges that shape a truly effective and innovative architecture. The result? A solution that lacks novelty and struggles to meet the real-world demands of scalability, efficiency, and adaptability.

Machine learning system design, especially in the context of generative AI, is about much more than the choice of data storage. It encompasses everything from clarifying ambiguous requirements to balancing trade-offs in system architecture, integrating complex components like large language models (LLMs) and vector stores, and refining performance through iterative feedback.

This blog takes you beyond the basics, outlining a systematic, advanced approach to ML system design with a spotlight on the unique demands of cutting-edge generative AI systems.

What is an ML System Design Problem?

An ML system design problem asks you to propose a practical, end-to-end machine learning solution to a real-world problem. Unlike traditional software system design, these problems prioritize data handling, algorithm selection, and deployment strategies over…