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.

* Dive into Kubeflow architecture and learn best practices for using the platform
* Understand the process of planning your Kubeflow deployment
* Install Kubeflow on an existing on-premises Kubernetes cluster
* Deploy Kubeflow on Google Cloud Platform step-by-step from the command line
* Use the managed Amazon Elastic Kubernetes Service (EKS) to deploy Kubeflow on AWS
* Deploy and manage Kubeflow across a network of Azure cloud data centers around the world

* Use KFServing to develop and deploy machine learning models

在部署机器学习应用时，构建模型只是故事的一小部分。整个过程涉及开发、协调、部署和运行可扩展和可移植的机器学习工作负载--Kubeflow让这个过程变得更加简单。这本实用的书向数据科学家、数据工程师和平台架构师展示了如何计划和执行Kubeflow项目，以使他们的Kubernetes工作流可移植和可扩展。

作者Josh Patterson、Michael Katzenellenbogen和Austin Harris展示了这个开源平台如何通过管理机器学习管道来协调工作流程。你将学会如何计划和执行Kubeflow平台，该平台可以支持从企业内部到云供应商（包括谷歌、亚马逊和微软）的工作流。

* 深入了解Kubeflow架构，学习使用该平台的最佳做法

* 了解规划你的Kubeflow部署的过程

* 在现有的企业内部Kubernetes集群上安装Kubeflow

* 在谷歌云平台上通过命令行一步步部署Kubeflow

* 使用管理的亚马逊Elastic Kubernetes服务（EKS）在AWS上部署Kubeflow

* 在世界各地的Azure云数据中心网络中部署和管理Kubeflow

* 使用KFServing来开发和部署机器学习模型