{"product_id":"distributed-machine-learning-patterns","title":"Distributed Machine Learning Patterns","description":"\u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003cdiv class=\"product-page-section\" style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003e   \u003cb style=\"box-sizing: border-box;\"\u003ePractical patterns for scaling machine learning from your laptop to a distributed cluster.\u003c\/b\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003eIn    \u003cspan style=\"box-sizing: border-box;\"\u003e\u003cb style=\"\"\u003eDistributed Machine Learning Patterns\u003c\/b\u003e\u003c\/span\u003e you will learn how to:   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cul style=\"box-sizing: border-box; margin-top: 0px; margin-bottom: 10.5px; padding-left: 17.5px;\"\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eApply distributed systems patterns to build scalable and reliable machine learning projects\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eConstruct machine learning pipelines with data ingestion, distributed training, model serving, and more\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eAutomate machine learning tasks with Kubernetes, TensorFlow, Kubeflow, and Argo Workflows\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eMake trade offs between different patterns and approaches\u003c\/li\u003e    \u003cli style=\"box-sizing: border-box;\"\u003eManage and monitor machine learning workloads at scale\u003c\/li\u003e   \u003c\/ul\u003e   \u003cdiv class=\"product-page-section\" style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003e    Scaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners.    \u003cb\u003e\u003ci\u003eDistributed Machine Learning Patterns\u003c\/i\u003e\u003c\/b\u003e teaches you how to scale machine learning models from your laptop to large distributed clusters.    \u003c\/div\u003e   \u003cdiv class=\"product-page-section\" style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003e    \u003cbr\u003e   \u003c\/div\u003e   \u003cdiv class=\"product-page-section\" style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003e    In    \u003cb\u003e\u003ci\u003eDistributed Machine Learning Patterns\u003c\/i\u003e\u003c\/b\u003e, you''ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines   \u003c\/div\u003e   \u003cbr style=\"box-sizing: border-box;\"\u003e   \u003cb\u003e\u003ci style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003eDistributed Machine Learning Patterns\u003c\/i\u003e \u003c\/b\u003eteaches you how to scale machine learning models from your laptop to large distributed clusters. In it, you''ll learn how to apply established distributed systems patterns to machine learning projects, and explore new ML-specific patterns as well. Firmly rooted in the real world, this book demonstrates how to apply patterns using examples based in TensorFlow, Kubernetes, Kubeflow, and Argo Workflows. Real-world scenarios, hands-on projects, and clear, practical DevOps techniques let you easily launch, manage, and monitor cloud-native distributed machine learning pipelines.  \u003c\/div\u003e \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003ch2 style=\"box-sizing: border-box; line-height: 1.1; color: inherit; margin: 0px 0px 10px; font-size: 27px; text-transform: lowercase;\"\u003eabout the technology\u003c\/h2\u003e  \u003ca name=\"about-the-technology\" class=\"anchor\" style=\"box-sizing: border-box; background-color: transparent; color: rgb(64, 127, 191); visibility: hidden; display: block; position: relative; margin-bottom: 0px; padding-bottom: 0px;\"\u003e\u003c\/a\u003eScaling up models from standalone devices to large distributed clusters is one of the biggest challenges faced by modern machine learning practitioners. Distributing machine learning systems allow developers to handle extremely large datasets across multiple clusters, take advantage of automation tools, and benefit from hardware accelerations. In this book, Kubeflow co-chair Yuan Tang shares patterns, techniques, and experience gained from years spent building and managing cutting-edge distributed machine learning infrastructure. \u003c\/div\u003e \u003cdiv class=\"product-page-section\" style='box-sizing: border-box; margin-bottom: 36px; color: rgb(51, 51, 51); font-family: Lato, -apple-system, BlinkMacSystemFont, \"avenir next\", avenir, \"helvetica neue\", helvetica, Ubuntu, roboto, noto, \"segoe ui\", arial, sans-serif; font-size: 17.25px; background-color: rgb(255, 255, 255);'\u003e  \u003ch2 style=\"box-sizing: border-box; line-height: 1.1; color: inherit; margin: 0px 0px 10px; font-size: 27px; text-transform: lowercase;\"\u003eabout the book\u003c\/h2\u003e  \u003ca name=\"about-the-book\" class=\"anchor\" style=\"box-sizing: border-box; background-color: transparent; color: rgb(64, 127, 191); visibility: hidden; display: block; position: relative;\"\u003e\u003c\/a\u003e  \u003ci style=\"box-sizing: border-box; margin-bottom: 0px; padding-bottom: 0px;\"\u003e\u003cb\u003eDistributed Machine Learning Patterns\u003c\/b\u003e\u003c\/i\u003e is filled with practical patterns for running machine learning systems on distributed Kubernetes clusters in the cloud. Each pattern is designed to help solve common challenges faced when building distributed machine learning systems, including supporting distributed model training, handling unexpected failures, and dynamic model serving traffic. Real-world scenarios provide clear examples of how to apply each pattern, alongside the potential trade offs for each approach. Once you''ve mastered these cutting edge techniques, you''ll put them all into practice and finish up by building a comprehensive distributed machine learning system. \u003c\/div\u003e","brand":"MediaPlace","offers":[{"title":"Default Title","offer_id":57526594535806,"sku":"NW9781617299025","price":63.0,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1379\/1261\/files\/9781617299025.jpg?v=1780959776","url":"https:\/\/mediaplace.com\/en-usa\/products\/distributed-machine-learning-patterns","provider":"MediaPlace","version":"1.0","type":"link"}