Ai And Business Rule Engines For Excel Power Users
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AI and Business Rule Engines for Excel Power Users
A power-packed manual to enhance your decision-making with the application of Business Rules using KIE, Drools, Kogito, MS Excel, Power Automate, Office Script, and MS Forms Purchase of the print or Kindle book includes a free PDF eBook Key FeaturesExplore the business rule tools by implementing real-world examples to write sophisticated rulesDiscover how decision services solve current business challenges using AICombine rules with workflows and scripting to deploy a cloud-based production environmentBook Description Microsoft Excel is widely adopted across diverse industries, but Excel Power Users often encounter limitations such as complex formulas, obscure business knowledge, and errors from using outdated sheets. They need a better enterprise-level solution, and this book introduces Business rules combined with the power of AI to tackle the limitations of Excel. This guide will give you a roadmap to link KIE (an industry-standard open-source application) to Microsoft's business process automation tools, such as Power Automate, Power Query, Office Script, Forms, VBA, Script Lab, and GitHub. You'll dive into the graphical Decision Modeling standard including decision tables, FEEL expressions, and advanced business rule editing and testing. By the end of the book, you'll be able to share your business knowledge as graphical models, deploy and execute these models in the cloud (with Azure and OpenShift), link them back to Excel, and then execute them as an end-to-end solution removing human intervention. You'll be equipped to solve your Excel queries and start using the next generation of Microsoft Office tools. What you will learnUse KIE and Drools decision services to write AI-based business rulesLink Business Rules to Excel using Power Query, Script Lab, Office Script, and VBABuild an end-to-end workflow with Microsoft Power Automate and Forms while integrating it with Excel and KogitoCollaborate on and deploy your decision models using OpenShift, Azure, and GitHubDiscover advanced editing using the graphical Decision Model Notation (DMN) and testing toolsUse Kogito to combine AI solutions with ExcelWho this book is for This book is for Excel power users, business users, and business analysts looking for a tool to capture their knowledge and deploy it as part of enterprise-grade systems. Working proficiency with MS Excel is required. Basic knowledge of web technologies and scripting would be an added advantage.
How to Build a Business Rules Engine
·This is the only book that demonstrates how to develop a business rules engine. Covers user requirements, data modeling, metadata, and more.·A sample application is used throughout the book to illustrate concepts. The code for the sample application is available online at http://www.refdataportal.com. ·Includes conceptual overview chapters suitable for management-level readers, including general introduction, business justification, development and implementation considerations, and more.·This is the only book that demonstrates how to develop a business rules engine. Covers user requirements, data modeling, metadata, and more.·A sample application is used throughout the book to illustrate concepts. The code for the sample application is available online at http://www.refdataportal.com. ·Includes conceptual overview chapters suitable for management-level readers, including general introduction, business justification, development and implementation considerations, and more.
Artificial Intelligence for Business
This book offers a practical guide to artificial intelligence (AI) techniques that are used in business. The book does not focus on AI models and algorithms, but instead provides an overview of the most popular and frequently used models in business. This allows the book to easily explain AI paradigms and concepts for business students and executives. Artificial Intelligence for Business is divided into six chapters. Chapter 1 begins with a brief introduction to AI and describes its relationship with machine learning, data science and big data analytics. Chapter 2 presents core machine learning workflow and the most effective machine learning techniques. Chapter 3 deals with deep learning, a popular technique for developing AI applications. Chapter 4 introduces recommendation engines for business and covers how to use them to be more competitive. Chapter 5 features natural language processing (NLP) for sentiment analysis focused on emotions. With the help of sentiment analysis, businesses can understand their customers better to improve their experience, which will help the businesses change their market position. Chapter 6 states potential business prospects of AI and the benefits that companies can realize by implementing AI in their processes.