GoKawiilNew Technical Book Teaches Foundation Model Engineering End-to-End
A new textbook titled Foundation Model Engineering has been released, aimed at AI engineers and research-minded readers who want to understand foundation models beyond basic API use. It ties together topics like attention, mixture-of-experts, RLHF, multimodality, long-context inference, retrieval-augmented generation, and agents into a single engineering narrative, using PyTorch examples, quizzes, and interactive visualizers.
GoKawiil's interpretation of the reporting above, not reported fact.
Most AI education resources treat these topics as disconnected trends, leaving engineers with terminology but not judgment about system-level trade-offs. By explaining how modeling choices, hardware constraints, and product needs interact, this kind of resource could help practitioners make better architectural decisions rather than just following best practices blindly.
- The book targets AI engineers and researchers who want deeper technical understanding beyond API-level usage.
- It connects historically disparate topics—transformers, MoE, RLHF, RAG, agents—into one coherent engineering framework.
- Learning materials include PyTorch code, quizzes, and interactive visualizers for hands-on comprehension.
Source: sungeuns.github.io — Seongeun So, 2026-09-14
Published there as: “Foundation Model Engineering: From Theory to Production”
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