{"product_id":"138405","title":"LLM Engineering ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPCf0uGL8nS2mrGrYRFgtCUJKtNP.png?v=1765062925\" style=\"max-width:100%;max-height:10px\"\u003e\u003c\/div\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\u003ccenter\u003e\n\n\u003cdiv style=\"width:95%\"\u003e\n\n\u003cdiv style=\"text-align:center;font-size:30px;font-weight:bolder;line-height:1.6em\"\u003e LLM Engineering \u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"border-bottom:1px;border-bottom-style:dotted;border-color:;padding-bottom:20px\"\u003e\u003ccenter\u003e\u003ctable align=\"center\" width=\"100%\"\u003e\u003ctbody style=\"border:0px\"\u003e\n\n\u003ctr\u003e\u003ctd align=\"center\" style=\"line-height:1.2em;text-align:center;font-size:18px;color:black;font-weight:bold;padding-bottom:20px;\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\u003ctr\u003e\u003ctd style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/image.yes24.com\/goods\/145962625\/XL\" style=\"max-width:100%;height:auto\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\n\n\n\u003c\/tbody\u003e\u003c\/table\u003e\u003c\/center\u003e\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;{split_style6}padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e Description \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;word-break:break-all;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eBook Introduction\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e\u003cdiv\u003e \u003cb\u003eA Practical Guide to All Things LLM Engineering\u003c\/b\u003e\u003cbr\u003e \u003cbr\u003e\"LLM Engineering\" provides a detailed guide to the engineering methods required to develop and deploy production-grade LLM applications. It systematically explores the LLM lifecycle, covering key concepts and practical techniques from data engineering to supervised learning fine-tuning, model evaluation, inference optimization, and RAG pipeline development.\u003cbr\u003e In this course, you will implement an AI that mimics an individual's writing style and personality through a real-world project called 'LLM Twin', and gain in-depth knowledge of practical LLM engineering know-how such as data collection, preprocessing, and model fine-tuning.\u003cbr\u003e By following the practical roadmap presented in this book, you will learn the entire process, from data collection to model optimization, step by step, and take your LLM engineering skills to the next level.\u003cbr\u003e\n\u003c\/div\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cul\u003e\u003cli\u003e You can preview some of the book's contents.\u003cbr\u003e \u003cspan\u003ePreview\u003c\/span\u003e\n\n\u003c\/li\u003e\u003c\/ul\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eindex\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e  \u003cdiv\u003e\n\u003cb\u003eCHAPTER 1 Understanding LLM Twin Concepts and Architecture\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _1.1 LLM Twin Concept\u003cbr\u003e _1.2 LLM Twin Product Planning\u003cbr\u003e _1.3 Development of ML systems based on feature, learning, and inference pipelines\u003cbr\u003e _1.4 System Architecture Design for LLM Twin\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 2 TOOLS AND INSTALLATION\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _2.1 Python Ecosystem and Project Installation\u003cbr\u003e _2.2 MLOps and LLMOps Tools\u003cbr\u003e _2.3 Database for storing unstructured data and vector data\u003cbr\u003e _2.4 Preparing to Use AWS\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 3 DATA ENGINEERING\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _3.1 Designing the LLM Twin Data Collection Pipeline\u003cbr\u003e _3.2 Implementing the LLM Twin Data Collection Pipeline\u003cbr\u003e _3.3 Collecting raw data into a data warehouse\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 4 RAG CHARACTERISTICS PIPELINE\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _4.1 Understanding RAG\u003cbr\u003e _4.2 Advanced RAG Overview\u003cbr\u003e _4.3 LLM Twin's RAG Characteristic Pipeline Architecture\u003cbr\u003e _4.4 Implementing the RAG feature pipeline for LLM Twin\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 5 Fine-Tuning Supervised Learning\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _5.1 Creating a Directive Dataset \u003cbr\u003e_5.2 Creating your own directive dataset\u003cbr\u003e _5.3 SFT technique\u003cbr\u003e _5.4 Practical Fine Tuning\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 6 Fine-tuning using preference sorting\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _6.1 Understanding the Preference Dataset\u003cbr\u003e _6.2 Creating a preference dataset\u003cbr\u003e _6.3 Preference sorting\u003cbr\u003e _6.4 DPO Implementation\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 7 LLM Evaluation\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _7.1 Model Evaluation\u003cbr\u003e _7.2 RAG Evaluation\u003cbr\u003e _7.3 TwinLlama-3.1-8B Evaluation\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 8 Inference Optimization\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _8.1 Model Optimization Strategy\u003cbr\u003e _8.2 Model Parallel Processing\u003cbr\u003e _8.3 Model Quantization\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 9 RAG Inference Pipeline\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _9.1 Understanding LLM Twin's RAG Inference Pipeline\u003cbr\u003e _9.2 Exploring Advanced RAG Techniques in LLM Twin\u003cbr\u003e _9.3 Implementing the RAG Inference Pipeline for LLM Twin\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 10 Deploying the Inference Pipeline\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _10.1 Distribution Type Selection Criteria\u003cbr\u003e _10.2 Understanding Inference Distribution Types\u003cbr\u003e _10.3 Comparison of Monolithic and Microservice Architectures\u003cbr\u003e _10.4 Exploring LLM Twin's Inference Pipeline Deployment Strategies\u003cbr\u003e _10.5 Deploying the LLM Twin Service \u003cbr\u003e_10.6 Autoscaling to handle spikes in usage\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eCHAPTER 11 MLOps and LLMOps\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _11.1 DevOps, MLOps, LLMOps\u003cbr\u003e _11.2 Deploying the LLM Twin Pipeline to the Cloud\u003cbr\u003e _11.3 Applying LLMOps to LLM Twin\u003cbr\u003e _summation\u003cbr\u003e _References\u003cbr\u003e\u003cbr\u003e \u003cb\u003eAPPENDIX MLOps Principles\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e _Principle 1: Automate or Operationalize\u003cbr\u003e _Principle 2: Version Control\u003cbr\u003e _Principle 3: Experimental Tracking\u003cbr\u003e _Principle 4: Test\u003cbr\u003e Principle 5: Monitoring\u003cbr\u003e Principle 6: Reproducibility\u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003eDetailed image\u003c\/b\u003e \u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\u003cdiv\u003e\u003cimg src=\"https:\/\/image.yes24.com\/momo\/TopCate5314\/MidCate001\/531306044.jpg\" border=\"0\" alt=\"Detailed Image 1\"\u003e\u003c\/div\u003e\u003c\/div\u003e\n\u003cbr\u003e\u003cdiv\u003e\u003ch5\u003e \u003cb\u003ePublisher's Review\u003c\/b\u003e\n\u003c\/h5\u003e\u003c\/div\u003e\n\u003cdiv\u003e\n\u003cdiv\u003e \u003cb\u003eDesign, implement, and learn your own AI\u003cbr\u003e Everything about RAG, fine-tuning (LoRA·QLoRA), FastAPI, and LLMOps\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ChatGPT is available to everyone, but it's not \"tailor-made\" for everyone.\u003cbr\u003e Generic writing, long-winded answers, and inconsistent output are not what we want from AI.\u003cbr\u003e This book goes beyond simple model calls and guides you through the entire process of developing a practical LLM system by implementing your own digital AI character, \"LLM Twin.\" \u003cbr\u003eFrom web scraping to RAG pipeline design, fine-tuning with LoRA and QLoRA, inference optimization, and cloud-based LLMOps, this book provides a hands-on project roadmap for developing end-to-end LLM applications.\u003cbr\u003e\u003cbr\u003e In this process, readers will experience everything from data design, infrastructure configuration, and deployment strategies required to complete a real-world, production-grade system.\u003cbr\u003e You can collect data from various sites like Medium, Substack, and GitHub, load it into MongoDB, optimize search performance using Qdrant, and even build microservices using RESTful APIs based on FastAPI.\u003cbr\u003e This book goes beyond simply explaining complex LLM techniques, but instead unfolds them in a form that can be directly applied in practice. It is a practical guide suited to an era where we are moving beyond simply \"utilizing\" AI to \"creating\" AI ourselves. \u003cbr\u003eIt will serve as a definitive guide for developers, AI engineers, and technology leaders seeking to perfect their own LLM system.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eWho is this book for?\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ● LLM Developer: Developers who want to go beyond using ChatGPT and create their own AI systems.\u003cbr\u003e ● AI Engineer: Professionals who want to practice the latest techniques such as RAG, LoRA, and QLoRA.\u003cbr\u003e ● ML System Engineer: Someone who wants to reliably deploy and operate AI services based on LLMOps\u003cbr\u003e ● Technology Leader: Team leaders who want to systematically learn the entire process of building an LLM, from data design to deployment.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eWhat do you mainly cover?\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ● LLM application architecture design: Planning and system configuration of the personalized AI character 'LLM Twin'\u003cbr\u003e ● Data collection and preprocessing: Web scraping + MongoDB\/Qdrant-based data storage and retrieval\u003cbr\u003e ● RAG Pipeline Development: Advanced Architecture Design and Document-Based Directive-Response Implementation \u003cbr\u003eSupervised Learning Fine-Tuning (SFT): Generating a Directive Dataset + Utilizing LoRA and QLoRA\u003cbr\u003e ● Direct Preference Optimization (DPO): Fine-tuning sorting based on user preferences\u003cbr\u003e ● Model Evaluation and Tuning: LLM·RAG Performance Measurement and TwinLlama Experiments\u003cbr\u003e ● Inference optimization: Improving real-time inference performance through quantization, parallel processing, etc.\u003cbr\u003e ● LLM application deployment: FastAPI server implementation + auto-scaling-based deployment\u003cbr\u003e ● LLMOps Practical Application: Operational Automation Strategies, Including Version Management and Monitoring \u003c\/div\u003e\n\u003cdiv\u003e\u003c\/div\u003e\n\u003c\/div\u003e\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cdiv style=\"width:95%;padding-top:20px;padding-bottom:20px\"\u003e\n\n\u003cdiv style=\"text-align:left;font-size:16px;font-weight:bold;padding-bottom:20px\"\u003e GOODS SPECIFICS \u003c\/div\u003e\n\n\u003cdiv style=\"text-align:left;font-size:14px;line-height:1.6em;\"\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eDate of issue:\u003c\/strong\u003e May 2, 2025\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003ePage count, weight, size:\u003c\/strong\u003e 508 pages | 918g | 183*235*22mm\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN13:\u003c\/strong\u003e 9791169213806\u003c\/div\u003e\n\n\u003cdiv style=\"width:100%;margin-bottom:5px;line-height:1.6em;font-size:14px\"\u003e - \u003cstrong\u003eISBN10:\u003c\/strong\u003e 1169213804 \u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\n\u003c\/div\u003e\n\n\u003ccenter\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003ccenter\u003e\u003ctable\u003e\u003ctr\u003e\u003ctd style=\"height:10px\"\u003e\u003c\/td\u003e\u003c\/tr\u003e\u003c\/table\u003e\u003c\/center\u003e\n\n\u003cspan\u003e\u003c\/span\u003e\n\n\u003c\/center\u003e\n\n\n\u003c\/center\u003e","brand":"LIBRAIRIE COREENNE","offers":[{"title":"Default Title","offer_id":43893236858922,"sku":"138405","price":49.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/4f404dca949a178296a5a7d62f9edb85.jpg?v=1765393292","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138405","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}