When deploying machine learning applications, building models is only a small part of the story. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads�a process Kubeflow makes much easier. With this practical guide, data scientists, data engineers, and platform architects will learn how to plan and execute a Kubeflow project that can support workflows from on-premises to the cloud. Kubeflow is an open source Kubernetes-native platform based on Google�s internal machine learning pipelines, and yet major cloud vendors including AWS and Azure advocate the use of Kubernetes and Kubeflow to manage containers and machine learning infrastructure. In today�s cloud-based world, this book is ideal for any team planning to build machine learning applications. With this book, you will: Get a concise overview of Kubernetes and Kubeflow Learn how to plan and build a Kubeflow installation Operate, monitor, and automate your installation Provide your Kubeflow installation with adequate security Serve machine learning models on Kubeflow