LLM Engineer's Handbook: Master the art of engineering large language models from concept to production
About this ebook
- Build and refine LLMs step by step, covering data preparation, RAG, and fine-tuning
- Learn essential skills for deploying and monitoring LLMs, ensuring optimal performance in production
- Utilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applications
- Implement robust data pipelines and manage LLM training cycles
- Create your own LLM and refine it with the help of hands-on examples
- Get started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoring
- Perform supervised fine-tuning and LLM evaluation
- Deploy end-to-end LLM solutions using AWS and other tools
- Design scalable and modularLLM systems
- Learn about RAG applications by building a feature and inference pipeline
This book is for AI engineers, NLP professionals, and LLM engineers looking to deepen their understanding of LLMs. Basic knowledge of LLMs and the Gen AI landscape, Python and AWS is recommended. Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios
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About the author
Paul Iusztin is a senior ML and MLOps engineer at Metaphysic, a leading GenAI platform, serving as one of their core engineers in taking their deep learning products to production. Along with Metaphysic, with over seven years of experience, he built GenAI, Computer Vision and MLOps solutions for CoreAI, Everseen, and Continental. Paul's determined passion and mission are to build data-intensive AI/ML products that serve the world and educate others about the process. As the Founder of Decoding ML, a channel for battle-tested content on learning how to design, code, and deploy production-grade ML, Paul has significantly enriched the engineering and MLOps community. His weekly content on ML engineering and his open-source courses focusing on end-to-end ML life cycles, such as Hands-on LLMs and LLM Twin, testify to his valuable contributions.
Maxime Labonne is a Senior Staff Machine Learning Scientist at Liquid AI, serving as the head of post-training. He holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and is recognized as a Google Developer Expert in AI/ML. An active blogger, he has made significant contributions to the open-source community, including the LLM Course on GitHub, tools such as LLM AutoEval, and several state-of-the-art models like NeuralBeagle and Phixtral. He is the author of the best-selling book “Hands-On Graph Neural Networks Using Python,” published by Packt. Connect with him on X and LinkedIn @maximelabonne.
