{"product_id":"140142","title":"There's a reason why machine learning teams are so successful. ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jYGWrp3j3YnQeiHzKTeT3puBUoluh.png?v=1765078401\" 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 There's a reason why machine learning teams are so successful. \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\/146897011\/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\u003eHow to analyze the structure of a machine learning project and connect it to optimal performance.\u003cbr\u003e It contains all the product development and management know-how and team operation strategies, including automated testing, refactoring, MLOps, and collaboration technologies!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e While the technology for training ML models is already widespread across many organizations, connecting them to products that deliver value to real customers remains a challenge. \u003cbr\u003eModels often remain in the PoC stage without being deployed, and even after months of development, projects can be stranded due to performance degradation, technical debt, and conflicts between teams.\u003cbr\u003e\u003cbr\u003e This book presents practical methods to solve such realistic problems.\u003cbr\u003e Beyond simple algorithms and tool usage, this book covers practical aspects of how teams plan, collaborate, and continuously improve products. From MLOps and CI\/CD to automated testing, container environment configuration, and team collaboration structures, it goes beyond simply \"how to excel in ML\" to answer the fundamental question of \"how ML teams should work.\" I confidently recommend this book to anyone considering team, culture, process, and organizational strategy beyond ML technology.\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\n\u003cdiv\u003e CHAPTER 01 Challenges and Better Directions in Providing ML Solutions \u003cbr\u003e_1.1 Expectations and Reality for ML\u003cbr\u003e _1.2 How to Use Systems Thinking and Lean\u003cbr\u003e _1.3 Conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[PART 01 PRODUCT AND DELIVERY]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e CHAPTER 02 Products and Delivery Techniques for ML Teams\u003cbr\u003e _2.1 ML product found\u003cbr\u003e _2.2 Getting Started: Preparing Your Team for Success\u003cbr\u003e _2.3 Product Delivery\u003cbr\u003e _2.4 Conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[PART 02 Engineering]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e CHAPTER 03 Effective Dependency Management: Principles and Tools\u003cbr\u003e _3.1 What if your code always worked, everywhere?\u003cbr\u003e _3.2 A brief introduction to Docker and batect\u003cbr\u003e _3.3 Conclusion\u003cbr\u003e\u003cbr\u003e CHAPTER 04 Effective Dependency Management in Practice\u003cbr\u003e _4.1 ML Development Workflow\u003cbr\u003e _4.2 Safe Dependency Management\u003cbr\u003e _4.3 Conclusion\u003cbr\u003e\u003cbr\u003e CHAPTER 05 Automated Testing: Going Fast and Avoiding Problems\u003cbr\u003e _5.1 Automated Testing: The Fundamentals of Fast and Reliable Iteration\u003cbr\u003e _5.2 Components of a Comprehensive Test Strategy for ML Systems\u003cbr\u003e _5.3 Software Testing\u003cbr\u003e _5.4 Conclusion\u003cbr\u003e\u003cbr\u003e CHAPTER 06 Automated Testing: Testing ML Models\u003cbr\u003e _6.1 Model Testing\u003cbr\u003e _6.2 Essential complementary techniques for model testing \u003cbr\u003e_6.3 Next Step: Applying What You've Learned\u003cbr\u003e _6.4 Conclusion\u003cbr\u003e\u003cbr\u003e CHAPTER 07 Using the Code Editor Effectively with Simple Techniques\u003cbr\u003e _7.1 The Benefits of Knowing an IDE (and Its Amazing Simplicity)\u003cbr\u003e _7.2 Plan: Increase Productivity in Two Steps\u003cbr\u003e _7.3 Conclusion\u003cbr\u003e\u003cbr\u003e CHAPTER 08 Refactoring and Technical Debt Management\u003cbr\u003e _8.1 Technical Debt: Sand in the Gears\u003cbr\u003e _8.2 How to refactor a notebook (or problematic codebase)\u003cbr\u003e _8.3 Managing Technical Debt in the Real World\u003cbr\u003e _8.4 Conclusion\u003cbr\u003e\u003cbr\u003e CHAPTER 09 Continuous Delivery for MLOps and ML (CD4ML)\u003cbr\u003e _9.1 MLOps' Strengths and Missing Puzzle Pieces\u003cbr\u003e _9.2 Continuous Delivery for ML (CD4ML)\u003cbr\u003e _9.3 How CD4ML Supports ML Governance and Responsible AI\u003cbr\u003e _9.4 Conclusion\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[PART 03 Team]\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e CHAPTER 10 ELEMENTS OF AN EFFECTIVE ML TEAM\u003cbr\u003e _10.1 Common Problems Facing ML Teams\u003cbr\u003e _10.2 Internal Components of an Effective Team\u003cbr\u003e _10.3 Improving Flow Through Engineering Efficiency\u003cbr\u003e _10.4 Conclusion\u003cbr\u003e\u003cbr\u003e CHAPTER 11 Effective ML Organizations\u003cbr\u003e _11.1 Common Challenges Facing ML Organizations \u003cbr\u003e_11.2 Effective organizational structure at the team level\u003cbr\u003e _11.3 Effective Leadership\u003cbr\u003e _11.4 Conclusion\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\/TopCate5369\/MidCate004\/536836735.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\u003eUncovering the secrets of top-performing machine learning teams!\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e With countless machine learning (ML) projects stalling at the PoC stage or failing due to poor performance and inter-team conflict, this book goes beyond simple technical solutions and offers a solution focused on team management and collaboration strategies. It covers the entire process from ML model development, productization, deployment, and continuous improvement, and contains practical methodologies that can be applied effectively in real-world projects.\u003cbr\u003e\u003cbr\u003e Large-scale language models (LLMs) have revolutionized ML and AI projects, facilitating automation and providing powerful foundational models. \u003cbr\u003eHowever, LLM is not a panacea for all problems, and traditional ML\/DL techniques are still often more appropriate.\u003cbr\u003e Additionally, effectively leveraging LLM requires a high level of expertise and management beyond simply calling APIs, including prompt engineering, fine-tuning, building a RAG (Augmented Search Generation) system, and validating and evaluating results.\u003cbr\u003e Traditional ML team operating principles and a systematic engineering approach are still essential to effectively perform these complex tasks.\u003cbr\u003e\u003cbr\u003e This book explains the latest engineering techniques, such as MLOps, CI\/CD, and automated testing, as well as specific practical strategies based on Lean principles and team collaboration strategies, to help ML teams and AI project teams continue to achieve results even amidst these changes. \u003cbr\u003eI recommend this book to all practitioners and leaders who want to maximize performance by approaching complex problems structurally.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eMain contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e ● ML product development method based on lean principles (reducing failures and repeating success)\u003cbr\u003e ● Practical Uses of MLOps and CI\/CD (How to Reduce Performance Degradation and Technical Debt)\u003cbr\u003e Automated testing, container environment configuration, and refactoring techniques (a practical ML product development process)\u003cbr\u003e ● Organizational structure and collaboration strategy for ML teams (team operation considering efficiency and effectiveness) \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 30, 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 484 pages | 183*235*19mm\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 9791169213875\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 1169213871 \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":43893401354282,"sku":"140142","price":43.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/1c669f863f3d11723ab3f632afd6f0f0.jpg?v=1765400389","url":"https:\/\/librairie.coreenne.fr\/en\/products\/140142","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}