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)
Section 1: The Basics
Section 2: Product Capabilities
Section 3: Technical Details

AI governance: Trust and transparency

AI is prevalent in every industry—everything from recommendation engines to tracking the spread of the COVID-19 virus. This application of AI empowers organizations to innovate and introduce efficiency, but it also introduces risks around how AI is employed to make business decisions.

In general, governance usually relates to who is responsible for data, how data is handled, who has access to see particular datasets in an enterprise, and how they get to use the data. This same notion of establishing a governance framework to protect the data and the consumers also applies to AI models, which may exhibit behavior that may be unfair or biased—or both—to some individuals or groups of people. Data governance has evolved over a period, and there are well-established methodologies and best practices, but we now need to do something similar for AI models. The challenge with traditional data governance is the increasing volume of...