LLM Engineers Handbook
Master the Art of Engineering Large Language Models from Concept to Production
Design, train, and deploy LLMs with MLOps best practices—data preparation, RAG, fine-tuning, and monitoring.
About the book
Packt's guide by a Metaphysic ML engineer and Liquid AI's Head of Post-Training. Covers building LLMs step-by-step (data, RAG, fine-tuning), MLOps best practices, preference alignment, evaluation, and inference optimization for cost-effective, scalable deployment.
Summary
Summary coming soon!