Kubeflow Operations Guide: Managing Cloud and On-Premise Deployment

by Josh Patterson, Michael Katzenellenbogen, Austin Harris

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. Authors Josh Patterson, Michael Katzenellenbogen, and Austin Harris demonstrate how this open source platform orchestrates workflows by 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.

301 pages, Paperback

Published January 12, 2021

Reviews

[Thang · Rating 4 out of 5]

I enjoy reading this book. Kubeflow and ecosystem's concepts and are easy to understand.
It would be better if it includes more modern stuff like Hyperparameter Turning.