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