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IBM Cloud Pak for Data

You're reading from   IBM Cloud Pak for Data An enterprise platform to operationalize data, analytics, and AI

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Product type Paperback
Published in Nov 2021
Publisher Packt
ISBN-13 9781800562127
Length 336 pages
Edition 1st Edition
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Authors (3):
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Hemanth Manda Hemanth Manda
Author Profile Icon Hemanth Manda
Hemanth Manda
Sriram Srinivasan Sriram Srinivasan
Author Profile Icon Sriram Srinivasan
Sriram Srinivasan
Deepak Rangarao Deepak Rangarao
Author Profile Icon Deepak Rangarao
Deepak Rangarao
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Toc

Table of Contents (17) Chapters Close

Preface 1. Section 1: The Basics
2. Chapter 1: The AI Ladder – IBM's Prescriptive Approach FREE CHAPTER 3. Chapter 2: Cloud Pak for Data: A Brief Introduction 4. Section 2: Product Capabilities
5. Chapter 3: Collect – Making Data Simple and Accessible 6. Chapter 4: Organize – Creating a Trusted Analytics Foundation 7. Chapter 5: Analyzing: Building, Deploying, and Scaling Models with Trust and Transparency 8. Chapter 6: Multi-Cloud Strategy and Cloud Satellite 9. Chapter 7: IBM and Partner Extension Services 10. Chapter 8: Customer Use Cases 11. Section 3: Technical Details
12. Chapter 9: Technical Overview, Management, and Administration 13. Chapter 10: Security and Compliance 14. Chapter 11: Storage 15. Chapter 12: Multi-Tenancy 16. Other Books You May Enjoy

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...

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