{"product_id":"kubeflow-for-machine-learning-from-lab-to-production","title":"Kubeflow for Machine Learning: From Lab to Production","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003eThis book provides a comprehensive guide to using Kubeflow for building production-grade machine learning implementations, covering topics such as design, core components, training, serving, and pipeline validation. It is written by experienced authors and includes examples and best practices to help data scientists and data engineers deploy machine learning models successfully. \u003c\/blockquote\u003e\u003cp\u003e                                                            \u003cstrong\u003eFormat\u003c\/strong\u003e: Paperback \/ softback\u003cbr\u003e                              \u003cstrong\u003eLength\u003c\/strong\u003e: 130 pages\u003cbr\u003e                              \u003cstrong\u003ePublication date\u003c\/strong\u003e: 20 October 2020\u003cbr\u003e                              \u003cstrong\u003ePublisher\u003c\/strong\u003e: O'Reilly Media, Inc, USA\u003cbr\u003e                          \u003c\/p\u003e \u003cp\u003e\u003cbr\u003eIf you're embarking on the journey of training a machine learning model but find yourself unsure about how to bring it to production, this comprehensive book is your ultimate guide. Kubeflow offers a suite of cloud-native tools designed to support every stage of a model's lifecycle, from data exploration and feature preparation to training and serving. This invaluable guide empowers data scientists to create robust and scalable machine learning implementations using Kubeflow, while also providing valuable insights for data engineers to ensure the reliability and scalability of their models.\u003cbr\u003e\u003cbr\u003eThroughout the book, a team of esteemed authors, including Holden Karau, Trevor Grant, Ilan Filonenko, Richard Liu, and Boris Lublinsky, elucidate the intricacies of Kubeflow and guide you step-by-step through the process of training and serving machine learning models on Kubernetes, whether in the cloud or in an on-premises development environment.\u003cbr\u003e\u003cbr\u003eUnderstanding Kubeflow's design, core components, and the problems it solves is crucial to your success. This book delves deep into Kubeflow's architecture, highlighting its key features and functionalities. You'll gain a deep understanding of how Kubeflow simplifies the process of building and deploying machine learning pipelines, making it an ideal platform for both novice and experienced practitioners.\u003cbr\u003e\u003cbr\u003eWith a focus on practicality, the book provides step-by-step instructions on how to set up Kubeflow on various cloud providers and on-premises clusters. You'll learn how to configure Kubeflow components, deploy machine learning pipelines, and monitor and optimize your models' performance. Whether you're working with popular tools like scikit-learn, TensorFlow, or Apache Spark, this book offers comprehensive guidance on integrating Kubeflow into your machine learning workflow.\u003cbr\u003e\u003cbr\u003eOne of the key strengths of Kubeflow is its ability to add custom stages, such as serving and prediction, to your machine learning pipelines. You'll discover how to integrate these stages seamlessly into your workflow, enabling you to deliver real-time predictions and insights to your users. The book also covers the importance of keeping your models up-to-date with Kubeflow Pipelines, a powerful tool for automating model updates and retraining.\u003cbr\u003e\u003cbr\u003eIn addition to its technical aspects, Kubeflow emphasizes the importance of validating machine learning pipelines. You'll learn how to design and implement validation frameworks, ensuring that your models are accurate and reliable before they are deployed in production. By following the examples and best practices outlined in this book, you'll be well-equipped to build production-grade machine learning implementations with Kubeflow and make a significant impact on your organization's data science and engineering efforts.\u003cbr\u003e\u003cbr\u003eSo, whether you're a data scientist looking to enhance your machine learning skills or a data engineer seeking to optimize your models' performance, this book is a must-read. With its comprehensive coverage, practical insights, and expert guidance, Kubeflow: A Guide to Building and Deploying Machine Learning Pipelines on Kubernetes will empower you to unlock the full potential of machine learning and drive innovation in your industry.\u003c\/p\u003e\u003cp\u003e                            \u003cstrong\u003eWeight\u003c\/strong\u003e: 466g                            \u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 177 x 234 x 19 (mm)                            \u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781492050124                                                      \u003c\/p\u003e","brand":"Grant Trevor,Holden Karau,Boris Lublinsky,Richard Liu,Ilan Filonenko","offers":[{"title":"Paperback \/ softback","offer_id":44100312039674,"sku":"9781492050124","price":28.55,"currency_code":"GBP","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/0698a2755e79df1ced9684c7ce058485.jpg?v=1621047978","url":"https:\/\/shulphink.com\/products\/kubeflow-for-machine-learning-from-lab-to-production","provider":"Shulph Ink","version":"1.0","type":"link"}