Foundation Model Engineering: From theory to production (sungeuns.github.io)

"Foundation Model Engineering" is a new technical textbook aimed at AI engineers and research-oriented individuals seeking to gain a comprehensive understanding of modern foundation models. The book moves beyond superficial API usage, delving into the engineering trade-offs and historical evolution of architectures, training pipelines, and systems constraints that influence contemporary AI design. Significant topics covered include attention mechanisms, mixture of experts (MoE), reinforcement learning with human feedback (RLHF), and retrieval-augmented generation (RAG). This resource is especially valuable for those looking to enhance their engineering judgment by connecting various modeling ideas with product requirements and system limitations. It offers rigorous conceptual explanations, hands-on PyTorch examples, interactive visualizers, and quizzes designed to deepen comprehension of critical factors like memory, throughput, and latency trade-offs. A notable aspect is its adaptability as a "living document," welcoming contributions to keep the content relevant amid the rapidly evolving AI landscape. This depth-focused approach positions it as an essential read for practitioners committed to developing or evaluating advanced AI systems.