KDnuggets Recommends 5 Books on Building and Deploying Large Language Models
Updated
Updated · KDnuggets · Jul 31
KDnuggets Recommends 5 Books on Building and Deploying Large Language Models
2 articles · Updated · KDnuggets · Jul 31
Summary
KDnuggets picked five books as a structured reading path for practitioners who want to move beyond API prompting into training, fine-tuning and deploying large language models.
The list spans the full workflow: Sebastian Raschka’s from-scratch guide, Andriy Burkov’s 100-page overview, Jay Alammar and Maarten Grootendorst’s hands-on manual, a Hugging Face-focused engineering reference, and an LLM operations handbook.
More technical selections emphasize concrete implementation details, including PyTorch transformer builds, over 20 annotated Jupyter notebooks, semantic search and retrieval-augmented generation, plus production deployment and evaluation patterns.
The article argues fragmented tutorials are no longer enough as transformer systems grow more complex, making book-length resources more useful for researchers, engineers and developers building production-ready AI applications.