{"product_id":"9781617299025","title":"Distributed Machine Learning Patterns","description":"\u003ctable\u003e\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eAuthor\/Contributor(s):\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eTang, Yuan\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003ePublisher:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003eManning\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eDate:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd\u003e1\/2\/2024\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eBinding:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003ePaperback\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003ctr\u003e\n\u003ctd style=\"\"\u003e\u003cstrong\u003eCondition:\u003c\/strong\u003e\u003c\/td\u003e\n\u003ctd style=\"\"\u003eNEW\u003cbr\u003e\n\u003c\/td\u003e\n\u003c\/tr\u003e\n\u003c\/tbody\u003e\u003c\/table\u003e\u003cb\u003ePractical patterns for scaling machine learning from your laptop to a distributed cluster.\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eDistributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. This book reveals best practice techniques and insider tips for tackling the challenges of scaling machine learning systems.\u003cbr\u003e\u003cbr\u003eIn \u003ci\u003eDistributed Machine Learning Patterns\u003c\/i\u003e you will learn how to:\u003cul\u003e \u003cli\u003eApply distributed systems patterns to build scalable and reliable machine learning projects\u003c\/li\u003e \u003cli\u003eBuild ML pipelines with data ingestion, distributed training, model serving, and more\u003c\/li\u003e \u003cli\u003eAutomate ML tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows\u003c\/li\u003e \u003cli\u003eMake trade-offs between different patterns and approaches\u003c\/li\u003e \u003cli\u003eManage and monitor machine learning workloads at scale\u003c\/li\u003e \u003c\/ul\u003e\u003cbr\u003eInside \u003ci\u003eDistributed Machine Learning Patterns\u003c\/i\u003e you’ll learn to apply established distributed systems patterns to machine learning projects—plus explore cutting-edge new patterns created specifically for machine learning. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Hands-on projects and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the technology\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eDeploying a machine learning application on a modern distributed system puts the spotlight on reliability, performance, security, and other operational concerns. In this in-depth guide, Yuan Tang, project lead of Argo and Kubeflow, shares patterns, examples, and hard-won insights on taking an ML model from a single device to a distributed cluster.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the book\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003ci\u003eDistributed Machine Learning Patterns\u003c\/i\u003e provides dozens of techniques for designing and deploying distributed machine learning systems. In it, you’ll learn patterns for distributed model training, managing unexpected failures, and dynamic model serving. You’ll appreciate the practical examples that accompany each pattern along with a full-scale project that implements distributed model training and inference with autoscaling on Kubernetes.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eWhat's inside\u003c\/b\u003e\u003cul\u003e \u003cli\u003eData ingestion, distributed training, model serving, and more\u003c\/li\u003e \u003cli\u003eAutomating Kubernetes and TensorFlow with Kubeflow and Argo Workflows\u003c\/li\u003e \u003cli\u003eManage and monitor workloads at scale\u003c\/li\u003e \u003c\/ul\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003eFor data analysts and engineers familiar with the basics of machine learning, Bash, Python, and Docker.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the author\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eYuan Tang\u003c\/b\u003e is a project lead of Argo and Kubeflow, maintainer of TensorFlow and XGBoost, and author of numerous open source projects.\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003cbr\u003ePART 1 BASIC CONCEPTS AND BACKGROUND\u003cbr\u003e1 Introduction to distributed machine learning systems\u003cbr\u003ePART 2 PATTERNS OF DISTRIBUTED MACHINE LEARNING SYSTEMS\u003cbr\u003e2 Data ingestion patterns\u003cbr\u003e3 Distributed training patterns\u003cbr\u003e4 Model serving patterns\u003cbr\u003e5 Workflow patterns\u003cbr\u003e6 Operation patterns\u003cbr\u003ePART 3 BUILDING A DISTRIBUTED MACHINE LEARNING WORKFLOW\u003cbr\u003e7 Project overview and system architecture\u003cbr\u003e8 Overview of relevant technologies\u003cbr\u003e9 A complete implementation","brand":"Manning","offers":[{"title":"Default Title","offer_id":44428951748863,"sku":"9781617299025","price":59.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0452\/0886\/2873\/files\/Jacket_c0bd7f3c-622e-469e-9c1e-59e22bc27d19.jpg?v=1771351514","url":"https:\/\/massivebookshop.com\/products\/9781617299025","provider":"MASSIVE BOOKSHOP","version":"1.0","type":"link"}