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

Predictive versus prescriptive analytics

Predictive analytics is a practice of using advanced algorithms and ML to process historical data, learning what has happened while uncovering unseen data patterns, interactions, and relationships. Predictive models provide actionable insights, but they don't say what action needs to be taken based on insights for best outcomes.

Prescriptive analytics, on the other hand, enable accurate decision-making for complex problems by using optimization models that are mathematical representations of business problems. These optimization models use solvers with sophisticated algorithms.

Often, we might question whether we should use predictive analytics or prescriptive analytics. The answer to that is we need both, and to illustrate the point, let's use a simple example. Throughout the Covid-19 pandemic, ML models have been used to forecast the demand for protective masks, but complementing this information with prescriptive analytics...