Openshift Multi Cluster Management Handbook
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OpenShift Multi-Cluster Management Handbook
Author: Giovanni Fontana
language: en
Publisher: Packt Publishing Ltd
Release Date: 2022-11-11
Discover best practices for designing and scaling robust OpenShift clusters’ architecture for different workloads Manage multiple clusters on-premise or in the cloud using multi-cluster management tools to keep them secure and compliant Implement multi-cluster CI/CD on OpenShift using GitOps Key Features Discover best practices to design robust OpenShift architecture and scale them to different workloads Understand the minimal collection of topics you should consider in your container security strategy Implement multi-cluster CI/CD on OpenShift using GitOps Book DescriptionFor IT professionals working with Red Hat OpenShift Container Platform, the key to maximizing efficiency is understanding the powerful and resilient options to maintain the software development platform with minimal effort. OpenShift Multi-Cluster Management Handbook is a deep dive into the technology, containing knowledge essential for anyone who wants to work with OpenShift. This book starts by covering the architectural concepts and definitions necessary for deploying OpenShift clusters. It then takes you through designing Red Hat OpenShift for hybrid and multi-cloud infrastructure, showing you different approaches for multiple environments (from on-premises to cloud providers). As you advance, you’ll learn container security strategies to protect pipelines, data, and infrastructure on each layer. You’ll also discover tips for critical decision making once you understand the importance of designing a comprehensive project considering all aspects of an architecture that will allow the solution to scale as your application requires. By the end of this OpenShift book, you’ll know how to design a comprehensive Red Hat OpenShift cluster architecture, deploy it, and effectively manage your enterprise-grade clusters and other critical components using tools in OpenShift Plus.What you will learn Understand the important aspects of OpenShift cluster architecture Design your infrastructure to run across hybrid clouds Define the best strategy for multitenancy on OpenShift Discover efficient troubleshooting strategies with OpenShift Build and deploy your applications using OpenShift Pipelines (Tekton) Work with ArgoCD to deploy your applications using GitOps practices Monitor your clusters’ security using Red Hat Advanced Cluster Security Who this book is for This book is for a wide range of IT professionals using or looking to use OpenShift with a hybrid/multi-cloud approach. In this book, IT architects will find practical guidance on OpenShift clusters’ architecture, while Sysadmins, SREs, and IT operators will learn more about OpenShift deployment, troubleshooting, networking, security, and tools to manage multiple clusters from a single pane. For DevOps engineers, this book covers CI/CD strategies for multiple clusters using GitOps. Equipped with just basic knowledge of containerization and Kubernetes, you’re ready to get started.
MLOps with Red Hat OpenShift
Build and manage MLOps pipelines with this practical guide to using Red Hat OpenShift Data Science, unleashing the power of machine learning workflows Key Features Grasp MLOps and machine learning project lifecycle through concept introductions Get hands on with provisioning and configuring Red Hat OpenShift Data Science Explore model training, deployment, and MLOps pipeline building with step-by-step instructions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMLOps with OpenShift offers practical insights for implementing MLOps workflows on the dynamic OpenShift platform. As organizations worldwide seek to harness the power of machine learning operations, this book lays the foundation for your MLOps success. Starting with an exploration of key MLOps concepts, including data preparation, model training, and deployment, you’ll prepare to unleash OpenShift capabilities, kicking off with a primer on containers, pods, operators, and more. With the groundwork in place, you’ll be guided to MLOps workflows, uncovering the applications of popular machine learning frameworks for training and testing models on the platform. As you advance through the chapters, you’ll focus on the open-source data science and machine learning platform, Red Hat OpenShift Data Science, and its partner components, such as Pachyderm and Intel OpenVino, to understand their role in building and managing data pipelines, as well as deploying and monitoring machine learning models. Armed with this comprehensive knowledge, you’ll be able to implement MLOps workflows on the OpenShift platform proficiently.What you will learn Build a solid foundation in key MLOps concepts and best practices Explore MLOps workflows, covering model development and training Implement complete MLOps workflows on the Red Hat OpenShift platform Build MLOps pipelines for automating model training and deployments Discover model serving approaches using Seldon and Intel OpenVino Get to grips with operating data science and machine learning workloads in OpenShift Who this book is for This book is for MLOps and DevOps engineers, data architects, and data scientists interested in learning the OpenShift platform. Particularly, developers who want to learn MLOps and its components will find this book useful. Whether you’re a machine learning engineer or software developer, this book serves as an essential guide to building scalable and efficient machine learning workflows on the OpenShift platform.
IBM Storage Fusion Product Guide
This IBM® Redbooks® publication offers a short overview of IBM's integrated environment for container workloads, IBM Storage Fusion. The product comes in two variants, IBM Storage Fusion HCI System including all required hardware, and IBM Storage Fusion SDS (software-only) for deployment in public or private Clouds. This paper has been updated with the 2.5.1 release only for IBM Storage Fusion and the 2.5.2 release of IBM Storage Fusion HCI System.