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 Features
- Explore data virtualization by accessing data in real time without moving it
- Unify the data and AI experience with the integrated end-to-end platform
- Explore the AI life cycle and learn to build, experiment, and operationalize trusted AI at scale
Book 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 learn
- Understand the importance of digital transformations and the role of data and AI platforms
- Get to grips with data architecture and its relevance in driving AI adoption using IBM's AI Ladder
- Understand Cloud Pak for Data, its value proposition, capabilities, and unique differentiators
- Delve into the pricing, packaging, key use cases, and competitors of Cloud Pak for Data
- Use the Cloud Pak for Data ecosystem with premium IBM and third-party services
- Discover IBM's vibrant ecosystem of proprietary, open-source, and third-party offerings from over 35 ISVs
Who 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.
Table of Contents
- The AI Ladder: IBM’s Prescriptive Approach
- Cloud Pak for Data: A Brief Introduction
- Collect - Making Data Simple and Accessible
- Organize - Creating a Trusted Analytics Foundation
- Analyzing: Building, Deploying, and Scaling Models with Trust and Transparency
- Multi-Cloud Strategy and Cloud Satellite
- IBM and Partner Extension Services
- Customer Use Cases
- Technical Overview, Management, and Administration
- Security and Compliance
- Storage
- Multi-Tenancy
Hemanth Manda heads product management at IBM and is responsible for the Cloud Pak for Data platform. He has broad experience in the technology and software industry spanning a number of strategy and execution roles over the past 20 years. In his current role, Hemanth leads a team of over 20 product managers responsible for simplifying and modernizing IBM's data and AI portfolio to support cloud-native architectures through the new platform offering that is Cloud Pak for Data. Among other things, he is responsible for rationalizing and streamlining the data and AI portfolio at IBM, a $6 billion-dollar business, and delivering new platform-wide capabilities through Cloud Pak for Data.
Sriram Srinivasan is an IBM Distinguished Engineer leading the architecture and development of Cloud Pak for Data. His interests lie in cloud-native technologies such as Kubernetes and their practical application for both client-managed environments and Software as a Service. Prior to this role, Sriram led the development of IBM Data Science Experience Local and the dashDB Warehouse as a Service for IBM Cloud. Early on in his career at IBM, Sriram led the development of various web and Eclipse tooling platforms, such as IBM Data Server Manager and the SQL Warehousing tool. He started his career at Informix, where he worked on application servers, database tools, e-commerce products, and Red Brick data warehouse.
Deepak Rangarao leads WW Technical Sales at IBM and is responsible for the Cloud Pak for Data platform. He has broad cross-industry experience in the data warehousing and analytics space, building analytic applications at large organizations and technical presales, both with start-ups and large enterprise software vendors. Deepak has co-authored several books on topics such as OLAP analytics, change data capture, data warehousing, and object storage and is a regular speaker at technical conferences. He is a certified technical specialist in Red Hat OpenShift, Apache Spark, Microsoft SQL Server, and web development technologies.
