Book description
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
Table of contents
- Preface
-
1. Introduction to Kubeflow
-
Machine Learning on Kubernetes
- The Evolution of Machine Learning in Enterprise
- Itâs Harder Than Ever to Run Enterprise Infrastructure
- Identifying Next-Generation Infrastructure (NGI) Core Principles
- Kubernetes for Production Application Deployment
- Enter: Kubeflow
- What Problems Does Kubeflow Solve?
- Origin of Kubeflow
- Who Uses Kubeflow?
- Common Kubeflow Use Cases
- Components of Kubeflow
- Summary
-
Machine Learning on Kubernetes
- 2. Kubeflow Architecture and Best Practices
- 3. Planning a Kubeflow Installation
- 4. Installing Kubeflow On-Premise
- 5. Running Kubeflow on Google Cloud
- 6. Running Kubeflow on Amazon Web Services
- 7. Running Kubeflow on Azure
- 8. Model Serving and Integration
-
A. Infrastructure Concepts
- Public Key Infrastructure
- Authentication
- Authorization
- Lightweight Directory Access Protocol
- Kerberos
- Transport Layer Security
- X.509 Cert
- Webhook
- Active Directory
- Identity Providers
- Identity-Aware Proxy (IAP)
- OAuth
- OpenID Connect
- End-User Authentication with JWT
- Simple and Protected GSS_API Negotiation Mechanism
- Dex: A Federated OpenID Connect Provider
- Service Accounts
- The Control Plane
- B. An Overview of Kubernetes
- C. Istio Operations and Kubeflow
- Index
Product information
- Title: Kubeflow Operations Guide
- Author(s):
- Release date: December 2020
- Publisher(s): O'Reilly Media, Inc.
- ISBN: 9781492053224
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