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LLM Engineer's Handbook: Master the art of engineering large language models from concept to production
Step into the world of LLMs with this practical guide that takes you from the fundamentals to deploying advanced applications using LLMOps best practices
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Key FeaturesBuild and refine LLMs step by step, covering data preparation, RAG, and fine-tuningLearn essential skills for deploying and monitoring LLMs, ensuring optimal performance in productionUtilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applicationsBook DescriptionArtificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems.
Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.
By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.
What you will learnImplement robust data pipelines and manage LLM training cyclesCreate your own LLM and refine it with the help of hands-on examplesGet started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoringPerform supervised fine-tuning and LLM evaluationDeploy end-to-end LLM solutions using AWS and other toolsDesign scalable and modularLLM systemsLearn about RAG applications by building a feature and inference pipelineWho this book is forThis 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
Table of ContentsUnderstanding the LLM Twin Concept and ArchitectureTooling and InstallationData EngineeringRAG Feature PipelineSupervised Fine-TuningFine-Tuning with Preference AlignmentEvaluating LLMsInference OptimizationRAG Inference PipelineInference Pipeline DeploymentMLOps and LLMOps
Get With Your PDF Copy, AI Assistant, and Next-Gen Reader Free
Key FeaturesBuild and refine LLMs step by step, covering data preparation, RAG, and fine-tuningLearn essential skills for deploying and monitoring LLMs, ensuring optimal performance in productionUtilize preference alignment, evaluation, and inference optimization to enhance performance and adaptability of your LLM applicationsBook DescriptionArtificial intelligence has undergone rapid advancements, and Large Language Models (LLMs) are at the forefront of this revolution. This LLM book offers insights into designing, training, and deploying LLMs in real-world scenarios by leveraging MLOps best practices. The guide walks you through building an LLM-powered twin that’s cost-effective, scalable, and modular. It moves beyond isolated Jupyter notebooks, focusing on how to build production-grade end-to-end LLM systems.
Throughout this book, you will learn data engineering, supervised fine-tuning, and deployment. The hands-on approach to building the LLM Twin use case will help you implement MLOps components in your own projects. You will also explore cutting-edge advancements in the field, including inference optimization, preference alignment, and real-time data processing, making this a vital resource for those looking to apply LLMs in their projects.
By the end of this book, you will be proficient in deploying LLMs that solve practical problems while maintaining low-latency and high-availability inference capabilities. Whether you are new to artificial intelligence or an experienced practitioner, this book delivers guidance and practical techniques that will deepen your understanding of LLMs and sharpen your ability to implement them effectively.
What you will learnImplement robust data pipelines and manage LLM training cyclesCreate your own LLM and refine it with the help of hands-on examplesGet started with LLMOps by diving into core MLOps principles such as orchestrators and prompt monitoringPerform supervised fine-tuning and LLM evaluationDeploy end-to-end LLM solutions using AWS and other toolsDesign scalable and modularLLM systemsLearn about RAG applications by building a feature and inference pipelineWho this book is forThis 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
Table of ContentsUnderstanding the LLM Twin Concept and ArchitectureTooling and InstallationData EngineeringRAG Feature PipelineSupervised Fine-TuningFine-Tuning with Preference AlignmentEvaluating LLMsInference OptimizationRAG Inference PipelineInference Pipeline DeploymentMLOps and LLMOps
783 pages, Kindle Edition
Published October 22, 2024
143 people are currently reading
334 people want to read
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Displaying 1 - 8 of 8 reviews
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October 10, 2024help me pls a african student eng i want to learn this boo
October 26, 2025
Solid coverage of LLM system concepts—feature pipelines, RAG optimization, MLOps. There's definitely useful stuff here.
The main issue is execution. The writing tends to over-explain trivial details while skipping over the interesting architectural decisions. You get a lot of what and how, but not much why or discussion of alternatives.
The code examples are a bit rough around the edges—inconsistent patterns, small bugs, implementations that feel more educational than practical. It's good for understanding the concepts and how pieces fit together, but you'll need to rethink things for real projects.
Worth reading if you want a conceptual overview of building LLM systems, just don't expect production-ready solutions.
The main issue is execution. The writing tends to over-explain trivial details while skipping over the interesting architectural decisions. You get a lot of what and how, but not much why or discussion of alternatives.
The code examples are a bit rough around the edges—inconsistent patterns, small bugs, implementations that feel more educational than practical. It's good for understanding the concepts and how pieces fit together, but you'll need to rethink things for real projects.
Worth reading if you want a conceptual overview of building LLM systems, just don't expect production-ready solutions.
March 16, 2025
A decent introduction to creating an LLM project with all the necessary steps. I liked the chapther on SFT, production grade RAG pipelines. The authors also managed to implement AbstractFactory, Strategy and Singleton design patterns as part of the project. I wish the code sections of the book were better formatted for readability. The print version of the book had code that was difficult to parse, of course Github versions were fine.
February 16, 2025
Most of the chapters i am more or less familiar, but the inference optimization chapter is worth my time, in particular, the quantization explanation is concise and easy to follow. Furthermore, i would shelf the the self-query which offers some easy explanation. the zenml pipeline part is less attractive to me.
May 13, 2025
i love how this book is written focussing on building end-to-end approach by understanding large language models into deployment. i really love the inference optimization and evaluation chapter that walk through the processes along with the code available. This book will be my future reference when i would like to do llm-based project or hackathon project going forward.
September 25, 2025
I'd say this book is valuable as a collection of brief pages on various topics you'll encounter while working as an "AI/ML/LLM Engineer", but there's little depth to it.
If you follow along a book that actually teaches the depth behind these, you'll get more return of investment (on your time spent).
If you follow along a book that actually teaches the depth behind these, you'll get more return of investment (on your time spent).
January 30, 2025
The book is more about engineering pipelines for different ML-related tasks. It actually assumes a lot of knowledge about ML, starting with assumptions that the reader knows what is “feature”, “inference”, etc. DNF.
August 2, 2025
Incredibly useful if you're developing a RAG or really any LLM-powered service from scratch. Highly recommend.
Displaying 1 - 8 of 8 reviews