Kubeflow Operations Guide: Managing Cloud and On-Premise Deployment (Paperback)

margin: 0px 0px 14px; padding: 0px; font-family: "Amazon Ember", Arial, sans-serif; font-size: 14px;'Building models is a small part of the story when it comes to deploying machine learning applications. The entire process involves developing, orchestrating, deploying, and running scalable and portable machine learning workloads--a process Kubeflow makes much easier. This practical book shows data scientists, data engineers, and platform architects how to plan and execute a Kubeflow project to make their Kubernetes workflows portable and scalable.'margin: -4px 0px 14px; padding: 0px; font-family: "Amazon Ember", Arial, sans-serif; font-size: 14px;'Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows managing machine learning pipelines. You'll learn how to plan and execute a Kubeflow platform that can support workflows from on-premises to cloud providers including Google, Amazon, and Microsoft.