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

Improving health advocacy program efficiency

Let's go through the use case by addressing the challenges that customers face in terms of improving the efficiency of their health advocacy program.

The customer challenge was for a non-profit health care provider network and health plan company that wanted to improve the efficiency of their health advocacy program. The goal was to identify members who are at risk and reduce the likelihood of members experiencing adverse events. They wanted to predict the likelihood of an Emergency Room (ER) visit in the next 9 months. Their challenge was to cater to the diverse data science teams with demands for different tools/technologies, as well as the support staff that maintain these tools/technologies. They also wanted to operationalize their ML models and scale them across all cohorts over the next year(s).

With Cloud Pak for Data, they were able to catalog information assets, including external datasets, with the centralized governance...