{"product_id":"138287","title":"Machine Learning System Design ","description":"\u003ccenter\u003e\u003cdiv style=\"text-align:center\"\u003e\u003cimg src=\"https:\/\/tmgdisk01.cafe24.com\/images\/vs\/4172\/sv\/3jXPB8vYuMHQsHXJs03JnssTlIxmGt.png?v=1765061876\" 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 Machine Learning System Design \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\/117843256\/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\u003eWhen dealing with machine learning in a production environment\u003cbr\u003e An MLOps Guide to Solving Countless Questions\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e Developing machine learning systems is a cyclical, not linear, process. \u003cbr\u003eEven after developing and deploying a model, continuous monitoring and updates are required.\u003cbr\u003e This book covers all the steps involved in designing and operating a machine learning system from a business perspective.\u003cbr\u003e The various approaches and case studies presented in the book provide insights for leading machine learning systems to success.\u003cbr\u003e The author's practice-oriented approach, based on his experience working with numerous companies, will serve as a foundation for solving the system's inherent challenges.\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 \u003cb\u003eChapter 1: Overview of Machine Learning Systems\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 1.1 When to Use Machine Learning\u003cbr\u003e 1.2 Understanding Machine Learning Systems\u003cbr\u003e 1.3 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 2: Introduction to Machine Learning System Design\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 2.1 The Purpose of Business and Machine Learning\u003cbr\u003e 2.2 Machine Learning System Requirements\u003cbr\u003e 2.3 Iterative Process\u003cbr\u003e 2.4 Structuring Machine Learning Problems \u003cbr\u003e2.5 Intelligence vs.\u003cbr\u003e data\u003cbr\u003e 2.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 3: Data Engineering Fundamentals\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 3.1 Data Sources\u003cbr\u003e 3.2 Data Format\u003cbr\u003e 3.3 Data Model\u003cbr\u003e 3.4 Data Storage Engine and Processing\u003cbr\u003e 3.5 Dataflow Mode\u003cbr\u003e 3.6 Batch Processing vs.\u003cbr\u003e Stream processing\u003cbr\u003e 3.7 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 4 Training Data\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 4.1 Sampling\u003cbr\u003e 4.2 Labeling\u003cbr\u003e 4.3 Class imbalance problem\u003cbr\u003e 4.4 Data Augmentation\u003cbr\u003e 4.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 5 Feature Engineering\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 5.1 Learned features vs.\u003cbr\u003e Engineered features\u003cbr\u003e 5.2 Feature Engineering Techniques\u003cbr\u003e 5.3 Data Leaks\u003cbr\u003e 5.4 How to design good features\u003cbr\u003e 5.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 6: Model Development and Offline Evaluation\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 6.1 Model Development and Training\u003cbr\u003e 6.2 Model Offline Evaluation\u003cbr\u003e 6.3 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 7: Model Deployment and Prediction Services\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 7.1 Common Sense About Machine Learning Deployment\u003cbr\u003e 7.2 Batch Prediction vs.\u003cbr\u003e Online predictions\u003cbr\u003e 7.3 Model Compression\u003cbr\u003e 7.4 Machine Learning in the Cloud and at the Edge\u003cbr\u003e 7.5 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 8: Data Distribution Shifts and Monitoring\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 8.1 Causes of Machine Learning System Failure\u003cbr\u003e 8.2 Data Distribution Shift \u003cbr\u003e8.3 Monitoring and Observability\u003cbr\u003e 8.4 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 9: Continuous Learning and Production Testing\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 9.1 Continuous Learning\u003cbr\u003e 9.2 Testing in Production\u003cbr\u003e 9.3 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 10: Infrastructure and Tools for MLOps\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 10.1 Storage and Computing\u003cbr\u003e 10.2 Development Environment\u003cbr\u003e 10.3 Resource Management\u003cbr\u003e 10.4 Machine Learning Platform\u003cbr\u003e 10.5 Build vs.\u003cbr\u003e purchase\u003cbr\u003e 10.6 Summary\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 11: The Human Side of Machine Learning\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e 11.1 User Experience\u003cbr\u003e 11.2 Team Structure\u003cbr\u003e 11.3 Responsible AI\u003cbr\u003e 11.4 Summary\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\/TopCate4135\/MidCate010\/413490203.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\u003eThe \"real\" machine learning story needed in the field.\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e When we think of machine learning, we usually think of model development and algorithms, but there's much more to consider when actually running machine learning in a production environment.\u003cbr\u003e We need to consider the data, features, model development, evaluation, deployment, monitoring, and infrastructure that make up a machine learning system from a holistic perspective. \u003cbr\u003eSince production machine learning is largely business-focused, business problem requirements and stakeholders are also important.\u003cbr\u003e\u003cbr\u003e This book is based on Stanford's CS329S: Machine Learning Systems Design, a leading course in the emerging field of MLOps.\u003cbr\u003e Author Chip Huyen, drawing on his experience deploying and operating machine learning at companies ranging from Netflix to startups, offers a variety of approaches to answering questions you've likely wondered about but struggled to find answers to.\u003cbr\u003e Rather than focusing on specific tool usage, we focus on the concepts, pros, cons, and tradeoffs of each machine learning technique, providing links to a wealth of resources to help you find more information quickly.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eTarget audience\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e - Machine learning practitioners: machine learning engineers, data scientists, machine learning platform engineers, engineering managers, etc. \u003cbr\u003e- Tool developers: If you want to identify areas where machine learning production is underserved and figure out how to build tools that fit into the ecosystem.\u003cbr\u003e - Job seekers and students: If you are looking for a job related to machine learning.\u003cbr\u003e - Technology and business leaders: Considering adopting machine learning solutions to improve products and business processes.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eKey contents by chapter\u003cbr\u003e\u003cbr\u003e Chapter 1: Overview of Machine Learning Systems\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e We'll explore various machine learning use cases and discuss when machine learning is appropriate and when it isn't.\u003cbr\u003e We compare production-ready machine learning with research-ready machine learning and traditional software.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Chapter 2: Introduction to Machine Learning System Design]\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e We explore an iterative process for defining system requirements based on business objectives and designing a machine learning system that satisfies them.\u003cbr\u003e We discuss how to structure machine learning problems.\u003cbr\u003e \u003cbr\u003e\u003cb\u003e[Chapter 3: Data Engineering Fundamentals]\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e We'll explore the various data sources and formats used in machine learning projects.\u003cbr\u003e Learn about data storage engines, major processing types, and different modes of passing data between processes.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Chapter 4 Training Data]\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e We explore techniques for obtaining high-quality training data.\u003cbr\u003e After exploring various sampling techniques, we discuss common challenges encountered when generating training data, including label multiplicity and class imbalance.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Chapter 5 Feature Engineering]\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e We explore feature engineering techniques and key considerations.\u003cbr\u003e We'll learn how to detect and prevent data leaks and discuss how to design good features.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Chapter 6: Model Development and Offline Evaluation]\u003cbr\u003e\u003c\/b\u003e \u003cbr\u003eWe'll explore useful tips for choosing the best algorithm for your task, then delve into various aspects of model development, including debugging, experiment tracking and versioning, distributed training, and AutoML.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Chapter 7: Model Deployment and Prediction Services]\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e We explore common myths surrounding machine learning deployment.\u003cbr\u003e After exploring online and batch prediction, we will explore various model compression techniques.\u003cbr\u003e We discuss how to deploy models on edge devices and in the cloud.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Chapter 8 Data Distribution Shift and Monitoring]\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e We discuss why machine learning models deployed in production fail.\u003cbr\u003e We examine the issue of data distribution shift, a topic of much discussion in both research and practice.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 9: Continuous Learning and Production Testing\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e We explore how to update machine learning models to adapt to shifts in data distribution. \u003cbr\u003eWe'll explore what continuous learning is and its challenges, discuss model retraining frequency, and production testing.\u003cbr\u003e\u003cbr\u003e \u003cb\u003e[Chapter 10 Infrastructure and Tools for MLOps]\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e Learn how to set up the right infrastructure for your machine learning system, depending on your production scale and circumstances.\u003cbr\u003e We discuss the four layers that make up infrastructure: storage, compute, resource management tools, machine learning platforms, and development environments.\u003cbr\u003e\u003cbr\u003e \u003cb\u003eChapter 11: The Human Side of Machine Learning\u003cbr\u003e\u003c\/b\u003e\u003cbr\u003e We discuss how the probabilistic nature of machine learning models impacts user experience.\u003cbr\u003e We explore organizational structures that enable members developing a system to collaborate effectively, and examine the impact of machine learning systems on society as a whole. \u003cbr\u003e\n\n\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 March 14, 2023\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 436 pages | 786g | 183*235*18mm\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 9791169210850\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 1169210856 \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":43893226733610,"sku":"138287","price":46.0,"currency_code":"EUR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0683\/2750\/5962\/files\/5c7e0ce44dee56664558d571886e6c7c.jpg?v=1765392619","url":"https:\/\/librairie.coreenne.fr\/en\/products\/138287","provider":"LIBRAIRIE COREENNE","version":"1.0","type":"link"}