Book Image

IBM Cloud Pak for Data

By : Hemanth Manda, Sriram Srinivasan, Deepak Rangarao
3 (1)
Book Image

IBM Cloud Pak for Data

3 (1)
By: Hemanth Manda, Sriram Srinivasan, Deepak Rangarao

Overview of this book

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.
Table of Contents (17 chapters)
1
Section 1: The Basics
4
Section 2: Product Capabilities
11
Section 3: Technical Details

What this book covers

Chapter 1, The AI Ladder: IBM's Prescriptive Approach, explores market dynamics, IBM's data and AI portfolio, and a detailed overview of the AI Ladder, what it entails, and how IBM offerings map to the different rungs of the ladder.

Chapter 2, Cloud Pak for Data: A Brief Introduction, covers IBM's modern data and AI platform in detail, along with some of its key differentiators. We will discuss Red Hat OpenShift, the implied cloud benefits it confers, and the platform foundational services that form the basis of Cloud Pak for Data.

Chapter 3, Collect – Making Data Simple and Accessible, offers a flexible approach to address the modern challenges with data-centric delivery, with the proliferation of data both in terms of volume and variety, with a mix of proprietary, open source, and third-party services.

Chapter 4, Organize – Creating a Trusted Analytics Foundation, allows you to learn how Cloud Pak for Data enables Data Ops (data operations), orchestration of people, processes, and technology to deliver trusted, business-ready data to data citizens, operations, applications, and artificial intelligence (AI) fast.

Chapter 5, Analyzing: Building, Deploying, and Scaling Models with Trust and Transparency, explains how to analyze your data in smarter ways and benefit from visualization and AI models that empower your organization to gain new insights and make better and smarter decisions.

Chapter 6, Multi-Cloud Strategy and Cloud Satellite, offers to operationalize AI throughout the business, allowing your employees to focus on higher-value work.

Chapter 7, IBM and Partner Extension Services, covers the technical concepts underpinning Cloud Pak for Data, including, but not limited to, an architecture overview, common services, Day-2 operations, infrastructure and storage support, and other advanced concepts.

Chapter 8, Customer Use Cases, drills down into the concepts of extension services, how they are packaged and priced, and the various IBM extension services available on Cloud Pak for Data across the Collect, Organize, Analyze, and Infuse rungs of the AI ladder.

Chapter 9, Technical Overview, Management, and Administration, addresses the importance of a partner ecosystem, the different tiers of business partners, and how clients can benefit from an open ecosystem on Cloud Pak for Data.

Chapter 10, Security and Compliance, focuses on the importance of business outcomes and key customer use case patterns of Cloud Pak for Data while highlighting the top three use case patterns: data modernization, DataOps, and an automated AI life cycle.

Chapter 11, Storage, looks at how the two critical prerequisites for enterprise adoption, security and governance, are addressed in Cloud Pak for Data.

Chapter 12, Multi-Tenancy, covers the different storage options supported by Cloud Pak for Data and how to configure it for high availability and disaster recovery.