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With IBM® Spectrum Virtualize and the Object-Based Access Control, you can implement multi-tenancy and secure storage usage in a Red Hat OpenShift environment. This IBM Redpaper® publication shows you how to secure the storage usage from the Openshift user to the IBM Spectrum® Virtualize array. You see how to restrict storage usage in a Red Hat Openshift Container Platform to avoid the over-consumption of storage by one or more user. These uses cases can be expanded to the use of this control to provide assistance with billing.
The purpose of this document is to show how to install RedHat OpenShift Container Platform (OCP) on Amazon web services (AWS) public cloud with OpenShift installer, a method that is known as Installer-provisioned infrastructure (IPI). We also describe how to validate the installation of IBM container storage interface (CSI) driver on OCP 4.2 that is installed on AWS. This document also describes the installation of OCP 4.x on AWS with customization and OCP 4.x installation on IBM cloud. This document discusses how to provision internet small computer system interface (iSCSI) storage that is made available by IBM Spectrum® Virtualize for Public Cloud (SVPC) that is deployed on AWS. Finally, the document discusses the use of Red Hat OpenShift command line interface (CLI), OCP web console graphical user interface (GUI), and AWS console.
This publication is intended to facilitate the deployment of the hybrid cloud business continuity solution with Red Hat OpenShift Container Platform and IBM® block CSI (Container Storage Interface) driver plug-in for IBM Spectrum® Virtualize on Public Cloud AWS (Amazon Web Services). This solution is designed to protect the data by using IBM Storage-based Global Mirror replication. For demonstration purposes, MySQL containerized database is installed on the on-premises IBM FlashSystem® that is connected to the Red Hat OpenShift Container Platform (OCP) cluster in the vSphere environment through the IBM block CSI driver. The volume (LUN) on IBM FlashSystem storage system is replicated by using global mirror on IBM Spectrum Virtualize for Public Cloud on AWS. Red Hat OpenShift cluster (OCP cluster) and the IBM block CSI driver plug-in are installed on AWS by using Installer-Provisioned Infrastructure (IPI) methodology. The information in this document is distributed on an as-is basis without any warranty that is either expressed or implied. Support assistance for the use of this material is limited to situations where IBM Spectrum Virtualize for Public Cloud is supported and entitled, and where the issues are specific to this Blueprint implementation.
RedHat OpenShift container platform is one of the leading enterprise-grade container orchestration platforms. It is designed for rapid deployment of web applications, databases, and microservices. Categorized as a container orchestration Platform as a Service (PaaS), it is based on open industry standards, such as the Container Runtime Interface - Open (CRI-O) and Kubernetes. OpenShift allow developers to focus on the code, while the platform manages the complex IT operations and processes. Although open-source, community-driven container orchestration platforms are available, such as OKD and Kubernetes, this IBM® Redpaper® publication focuses on Red Hat OpenShift. It describes the basic concepts of OpenShift persistent storage architecture and its integration into IBM Cloud® Paks. The deployment of the IBM block storage CSI driver also is discussed. This publication also describes the concepts, technology and current working practices for installing the Container Storage Interface (CSI) plug-in for Kubernetes to use IBM Enterprise Storage platforms for persistent storage coupled with Red Hat OpenShift Container Platform (OCP). This publication also provides an overview of containers, Kubernetes, and Openshift for context (it is expected that the reader has a working knowledge of these underlying technologies). It also includes architectural examples of the orchestration platform will be given. This paper serves as a guide about how to deploy the CSI driver for block storage by using the DS8000® and Spectrum Virtualize platforms as persistent storage in a Red Hat OpenShift platform. The publication is intended for storage administrators, IT architects, OpenShift technical specialists and anyone who wants to integrate IBM Enterprise storage on OpenShift V4.3/4.4/4.5 on IBM Power, IBM Z®, and x86 systems.
Cybersecurity is the most important arm of defense against cyberattacks. With the recent increase in cyberattacks, corporations must focus on how they are combating these new high-tech threats. When establishing best practices, a corporation must focus on employees' access to specific workspaces and information. IBM Z® focuses on allowing high processing virtual environments while maintaining a high level of security in each workspace. Organizations not only need to adjust their approach to security, but also their approach to IT environments. To meet new customer needs and expectations, organizations must take a more agile approach to their business. IBM® Z allows companies to work with hybrid and multi-cloud environments that allows more ease of use for the user and efficiency overall. Working with IBM Z, organizations can also work with many databases that are included in IBM Cloud Pak® for Data. IBM Cloud Pak for Data allows organizations to make more informed decisions with improved data usage. Along with the improved data usage, organizations can see the effects from working in a Red Hat OpenShift environment. Red Hat OpenShift is compatible across many hardware services and allows the user to run applications in the most efficient manner. The purpose of this IBM Redbooks® publication is to: Introduce IBM Z and LinuxONE platforms and how they work with the Red Hat OpenShift environment and IBMCloud Pak for Data Provide examples and the uses of IBM Z with Cloud Paks for Data that show data gravity, consistent development experience, and consolidation and business resiliency The target audience for this book is IBM Z Technical Specialists, IT Architects, and System Administrators.
Enables readers to understand mathematical concepts behind data engineering and machine learning algorithms and apply them using open-source Python libraries Machine Learning Theory and Applications delves into the realm of machine learning and deep learning, exploring their practical applications by comprehending mathematical concepts and implementing them in real-world scenarios using Python and renowned open-source libraries. This comprehensive guide covers a wide range of topics, including data preparation, feature engineering techniques, commonly utilized machine learning algorithms like support vector machines and neural networks, as well as generative AI and foundation models. To facilitate the creation of machine learning pipelines, a dedicated open-source framework named hephAIstos has been developed exclusively for this book. Moreover, the text explores the fascinating domain of quantum machine learning and offers insights on executing machine learning applications across diverse hardware technologies such as CPUs, GPUs, and QPUs. Finally, the book explains how to deploy trained models through containerized applications using Kubernetes and OpenShift, as well as their integration through machine learning operations (MLOps). Additional topics covered in Machine Learning Theory and Applications include: Current use cases of AI, including making predictions, recognizing images and speech, performing medical diagnoses, creating intelligent supply chains, natural language processing, and much more Classical and quantum machine learning algorithms such as quantum-enhanced Support Vector Machines (QSVMs), QSVM multiclass classification, quantum neural networks, and quantum generative adversarial networks (qGANs) Different ways to manipulate data, such as handling missing data, analyzing categorical data, or processing time-related data Feature rescaling, extraction, and selection, and how to put your trained models to life and production through containerized applications Machine Learning Theory and Applications is an essential resource for data scientists, engineers, and IT specialists and architects, as well as students in computer science, mathematics, and bioinformatics. The reader is expected to understand basic Python programming and libraries such as NumPy or Pandas and basic mathematical concepts, especially linear algebra.
IBM® Spectrum Virtualize is a key member of the IBM Spectrum® Storage portfolio. It is a highly flexible storage solution that enables rapid deployment of block storage services for new and traditional workloads, whether on-premises, off-premises, or a combination of both. The initial release of IBM Spectrum Virtualize for Public Cloud is now available on Amazon Web Services (AWS). This IBM RedpaperTM publication gives a broad understanding of the IBM Spectrum Virtualize for Public Cloud on AWS architecture. It also provides planning and implementation information about the common use cases for this new product. This publication helps storage and networking administrators plan, implement, install, modify, and configure the IBM Spectrum Virtualize for Public Cloud on AWS offering Version 8.3.1. It also provides a detailed description of troubleshooting tips.
This IBM® Redbooks® publication captures several of the preferred practices and describes the performance gains that can be achieved by implementing the IBM SAN Volume Controller powered by IBM Spectrum® Virtualize Version 8.4.2. These practices are based on field experience. This book highlights configuration guidelines and preferred practices for the storage area network (SAN) topology, clustered system, back-end storage, storage pools and managed disks, volumes, Remote Copy services and hosts. It explains how you can optimize disk performance with the IBM System Storage Easy Tier® function. It also provides preferred practices for monitoring, maintaining, and troubleshooting. This book is intended for experienced storage, SAN, IBM FlashSystem®, IBM SAN Volume Controller, and IBM Storwize® administrators and technicians. Understanding this book requires advanced knowledge of these environments.
This IBM® Redbooks® publication captures several of the preferred practices and describes the performance gains that can be achieved by implementing the IBM FlashSystem® products that are powered by IBM Spectrum® Virtualize Version 8.4.2. These practices are based on field experience. This book highlights configuration guidelines and preferred practices for the storage area network (SAN) topology, clustered system, back-end storage, storage pools and managed disks, volumes, Remote Copy services, and hosts. It explains how you can optimize disk performance with the IBM System Storage Easy Tier® function. It also provides preferred practices for monitoring, maintaining, and troubleshooting. This book is intended for experienced storage, SAN, IBM FlashSystem, SAN Volume Controller, and IBM Storwize® administrators and technicians. Understanding this book requires advanced knowledge of these environments.
Build end-to-end AI solutions with IBM Cloud Pak for Data to operationalize AI on a secure platform based on cloud-native reliability, cost-effective multitenancy, and efficient resource management Key FeaturesExplore data virtualization by accessing data in real time without moving itUnify the data and AI experience with the integrated end-to-end platformExplore the AI life cycle and learn to build, experiment, and operationalize trusted AI at scaleBook Description Cloud Pak for Data is IBM's modern data and AI platform that includes strategic offerings from its data and AI portfolio delivered in a cloud-native fashion with the flexibility of deployment on any cloud. The platform offers a unique approach to addressing modern challenges with an integrated mix of proprietary, open-source, and third-party services. You'll begin by getting to grips with key concepts in modern data management and artificial intelligence (AI), reviewing real-life use cases, and developing an appreciation of the AI Ladder principle. Once you've gotten to grips with the basics, you will explore how Cloud Pak for Data helps in the elegant implementation of the AI Ladder practice to collect, organize, analyze, and infuse data and trustworthy AI across your business. As you advance, you'll discover the capabilities of the platform and extension services, including how they are packaged and priced. With the help of examples present throughout the book, you will gain a deep understanding of the platform, from its rich capabilities and technical architecture to its ecosystem and key go-to-market aspects. By the end of this IBM book, you'll be able to apply IBM Cloud Pak for Data's prescriptive practices and leverage its capabilities to build a trusted data foundation and accelerate AI adoption in your enterprise. What you will learnUnderstand the importance of digital transformations and the role of data and AI platformsGet to grips with data architecture and its relevance in driving AI adoption using IBM's AI LadderUnderstand Cloud Pak for Data, its value proposition, capabilities, and unique differentiatorsDelve into the pricing, packaging, key use cases, and competitors of Cloud Pak for DataUse the Cloud Pak for Data ecosystem with premium IBM and third-party servicesDiscover IBM's vibrant ecosystem of proprietary, open-source, and third-party offerings from over 35 ISVsWho this book is for This book is for data scientists, data stewards, developers, and data-focused business executives interested in learning about IBM's Cloud Pak for Data. Knowledge of technical concepts related to data science and familiarity with data analytics and AI initiatives at various levels of maturity are required to make the most of this book.