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.

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Table of Contents (17 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the color images
Conventions used
Get in touch
Share Your Thoughts
1
Section 1: The Basics
Section 1: The Basics
Free Chapter
2
Chapter 1: The AI Ladder – IBM's Prescriptive Approach
Chapter 1: The AI Ladder – IBM's Prescriptive Approach
Market dynamics and IBM's Data and AI portfolio
Introduction to the AI ladder
Collect – making data simple and accessible
Organize – creating a trusted analytics foundation
Analyze – building and scaling models with trust and transparency
Infuse – operationalizing AI throughout the business
Summary
3
Chapter 2: Cloud Pak for Data: A Brief Introduction
Chapter 2: Cloud Pak for Data: A Brief Introduction
The case of a data and AI platform – recap
Overview of Cloud Pak for Data
Exploring unique differentiators, key use cases, and customer adoption
Cloud Pak for Data: additional details
Red Hat OpenShift
Summary
4
Section 2: Product Capabilities
Section 2: Product Capabilities
5
Chapter 3: Collect – Making Data Simple and Accessible
Chapter 3: Collect – Making Data Simple and Accessible
Data – the world's most valuable asset
Challenges with data-centric delivery
Enterprise data architecture
Data virtualization – accessing data anywhere
Data virtualization versus ETL – when to use what?
Platform connections – streamlining data connectivity
Data estate modernization using Cloud Pak for Data
Summary
6
Chapter 4: Organize – Creating a Trusted Analytics Foundation
Chapter 4: Organize – Creating a Trusted Analytics Foundation
Introducing Data Operations (DataOps)
Organizing enterprise information assets
Establishing metadata and stewardship
Profiling to get a better understanding of your data
Classifying data for completeness
Enabling trust with data quality
Data privacy and activity monitoring
Data integration at scale
Master data management
Extending MDM toward a Digital Twin
Summary
7
Chapter 5: Analyzing: Building, Deploying, and Scaling Models with Trust and Transparency
Chapter 5: Analyzing: Building, Deploying, and Scaling Models with Trust and Transparency
Self-service analytics of governed data
BI and reporting
Predictive versus prescriptive analytics
Understanding AI
AI life cycle – Transforming insights into action
AI governance: Trust and transparency
Automating the AI life cycle using Cloud Pak for Data
Summary
8
Chapter 6: Multi-Cloud Strategy and Cloud Satellite
Chapter 6: Multi-Cloud Strategy and Cloud Satellite
IBM's multi-cloud strategy
Supported deployment options
Cloud Pak for Data as a Service
IBM Cloud Satellite
A data fabric for a multi-cloud future
Summary
9
Chapter 7: IBM and Partner Extension Services
Chapter 7: IBM and Partner Extension Services
IBM and third-party extension services
Collect extension services
Organize extension services
Infuse cartridges
Modernization upgrades to Cloud Pak for Data cartridges
Summary
10
Chapter 8: Customer Use Cases
Chapter 8: Customer Use Cases
Improving health advocacy program efficiency
Voice-enabled chatbots
Risk and control automation
Enhanced border security
Unified Data Fabric
Financial planning and analytics
Summary
11
Section 3: Technical Details
Section 3: Technical Details
12
Chapter 9: Technical Overview, Management, and Administration
Chapter 9: Technical Overview, Management, and Administration
Technical requirements
Architecture overview
Infrastructure requirements, storage, and networking
Foundational services and the control plane
Multi-tenancy, resource management, and security
Day 2 operations
Summary
References
13
Chapter 10: Security and Compliance
Chapter 10: Security and Compliance
Technical requirements
Security and Privacy by Design
Secure operations in a shared environment
User access and authorizations
Meeting compliance requirements
Summary
References
14
Chapter 11: Storage
Chapter 11: Storage
Understanding the concept of persistent volumes
Off-cluster storage
Operational considerations
Summary
Further reading
15
Chapter 12: Multi-Tenancy
Chapter 12: Multi-Tenancy
Tenancy considerations
Architecting for multi-tenancy
Reviewing the tenancy requirements
In-namespace sub-tenancy with looser isolation
Summary
Why subscribe?
16
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Automating the AI life cycle using Cloud Pak for Data

After an organization has been able to collect its data and organize it using a trusted governance catalog, it can now tap into the data to build and scale AI models across the business. To build AI models from the ground up and scale it across the business, organizations need capabilities covering the full AI life cycle, and this includes the following:

  • Build—This is where companies build their AI models.
  • Run—After a model has been built, it needs to be put into production within an application or a business process.
  • Manage—After a model is built and running, the question becomes: How can it be scaled with trust and transparency? To address complex build and run environments, enterprises need a tool that not only manages the environment but also explains how their models arrived at their predictions.

Let's exercise Model Operations (ModelOps) using Cloud Pak for Data to understand...

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