You're reading from AI and Business Rule Engines for Excel Power Users Capture and scale your business knowledge into the cloud – with Microsoft 365, Decision Models, and AI tools from IBM and Red Hat

Product type Paperback

Published in Mar 2023

Publisher Packt

ISBN-13 9781804619544

Length 386 pages

Edition 1st Edition

Languages

Power Query

Tools

Excel

Concepts

Information Management

Authors (2):

Paul Browne (GBP)

ALEX PORCELLI

View More author details

Table of Contents (22) Chapters

Preface

1. Part 1:The Problem with Excel, and Why Rule-Based AI Can Be the Solution FREE CHAPTER

2. Chapter 1: Wrestling with Excel? You Are Not Alone

3. Chapter 2: Choosing an AI and Business Rules Engine – Why Drools and KIE?

4. Chapter 3: Your First Business Rule with the Online KIE Sandbox

5. Part 2: Writing Business Rules and Decision Models – with Real-Life Examples

6. Chapter 4: More Decision Models, Business Rules, and Decision Tables

7. Chapter 5: Sharing and Deploying Decision Models Using OpenShift and GitHub

8. Chapter 6: Calling Business Rules from Excel Using Power Query

9. Part 3: Extending Excel, Decision Models, and Business Process Automation into a Complete Enterprise Solution

10. Chapter 7: Using Business Rules in Excel with Visual Basic, Script Lab, or Office Scripts

11. Chapter 8: Using AI and Decision Services Within Power Automate Workflows

12. Chapter 9: Advanced Expressions, Decision Models, and Testing

13. Part 4: Next Steps in AI, Machine Learning, and Rule Engines

14. Chapter 10: Scaling Rules in Business Central with Docker and the Cloud

15. Chapter 11: Rules-Based AI and Machine Learning AI – Combining the Best of Both

16. Chapter 12: What Next? A Look inside Neural Networks, Enterprise Projects, Advanced Rules, and the Rule Engine

17. Index

Why subscribe?

18. Other Books You May Enjoy

Appendix A - Introduction to Visual Basic for Applications

Introduction to macros and VBA

1. Appendix B - Testing Using VSCode, Azure, and GitHub Codespaces

2. Appendix C - Troubleshooting Docker

Make your decision service easy to work with

You might not have realized it, but in the previous chapter, you wrote your first decision service. When running our Greeting Example in the KIE Sandbox, we supplied inputs (a name and if it was morning or not), made a decision, and then shared the output (Good Morning or Good Afternoon).

While we can’t see many colleagues signing up to use this particular service, this will change quickly as we move into the more sophisticated examples in this chapter. It’s worth thinking now about what other people need to know when using our service:

How we make a decision – in general, the Decision Model and Notation (DMN) format makes this pretty clear. It is certainly much clearer than other solutions (such as functions hidden in code or in Excel).
The data needed to be passed into the decision service, and the format of the data we get back from it.

In our greetings service, we expected a String (Text name...