Power Platform and the AI Revolution E-Book

Aaron Guilmette

• Herausgeber: Packt Publishing
• Kategorie: Fachliteratur
• Sprache: Englisch

Seitenzahl: 263

Veröffentlichungsjahr: 2024

Beschreibung

In this AI era, employing leading machine learning and AI models such as ChatGPT for responding to customer feedback and prototyping applications is crucial to drive business success in the competitive market. This book is an indispensable guide to integrating cutting-edge technology into business operations and leveraging AI to analyze sentiment at scale, helping free up valuable time to enhance customer relationships.
Immerse yourself in the future of AI-enabled application development by working with Power Automate, Power Apps, and the new Copilot Studio. With this book, you’ll learn foundational AI concepts as you explore the extensive capabilities of the low-code Power Platform. You’ll see how Microsoft's advanced machine learning technologies can streamline common business tasks such as extracting key data elements from customer documents, reviewing customer emails, and validating passports and drivers’ licenses. The book also guides you in harnessing the power of generative AI to expedite tasks like creating executive summaries, building presentations, and analyzing resumes. You’ll build apps using natural language prompting and see how ChatGPT can be used to power chatbots in your organization.
By the end of this book, you’ll have charted your path to developing your own reusable AI automation patterns to propel your business operations into the future.

Leseprobe

Power Platform and the AI Revolution

Explore modern AI services to develop apps, bots, and automation patterns to enhance customer experiences

Aaron Guilmette

Power Platform and the AI Revolution

Copyright © 2024 Packt Publishing

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First published: May 2024

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ISBN 978-1-83508-636-0

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I’d like to thank Microsoft and OpenAI for gently introducing us to our robot overlords. Be good to them as, one day, they’ll put us in people zoos. I also want to acknowledge the support of my long-suffering girlfriend, Christine, who has put up with my book deadlines interrupting vacations for my last 15 books. She’s the real MVP here.

Finally, I’d like to thank my kids—Liberty, Hudson, Anderson, Glory, and Victory. Without them, I’d probably be able to retire to a tropical location with umbrella drinks a lot sooner.

– Aaron Guilmette

Contributors

About the author

Aaron Guilmette is a principal architect at Planet Technologies, an award-winning Microsoft Partner focused on dragging public sector customers into the modern era. Previously, he worked at Microsoft as a senior program manager for Microsoft 365 Customer Experience. As an author of at least 15 other IT books you’ve probably seen recommended on Amazon, he specializes in identity, messaging, and automation technologies.

When he’s not writing books or tools for his customers, trying to teach one of his kids to drive, or making tacos with his girlfriend, Aaron can be found tinkering with cars and “investing” in Star Wars memorabilia. You can visit his blog at https://aka.ms/aaronblog or connect with him on LinkedIn at https://www.linkedin.com/in/aaronguilmette.

About the reviewer

Steve Miles is CTO at Westcoast Cloud, part of a multi-billion turnover IT distributor based in the UK and Ireland. Steve is a Microsoft Azure Most Valuable Professional (MVP), Microsoft Certified Trainer (MCT), and an Alibaba Cloud MVP. Steve has over 20 years of technology experience along with his previous military career in engineering, signals, and communications.

Among other books, Steve is the author of the number 1 Amazon best-selling AZ-900 certification book titled Microsoft Azure Fundamentals and Beyond. Like Aaron, he is also a petrolhead, and can also be found tinkering with cars when he is not writing. You can connect with him on LinkedIn athttps://www.linkedin.com/in/stevemiles70/.

Ahmad Najjar is a seasoned Power Platform Lead Architect at Avanade, based in Oslo, Norway. With a remarkable career spanning 20 years in the Microsoft development ecosystem, Ahmad has honed his expertise in C#, ASP.NET, SQL, and SharePoint. Over the last decade, he has also accumulated extensive experience with Azure technologies, including Logic Apps, API Management, and Function Apps.

For the past nine years, Ahmad has been deeply immersed in the Power Platform, specializing in Power Automate, Power Apps, and AI Builder. His role as an enthusiastic solution architect doesn't prevent him from being a passionate developer at heart which earned him a reputation for excellence and innovation.

Ahmad's accolades include being a FastTrack Recognized Solution Architect in both Power Apps and Power Automate, as well as a Business Applications MVP for seven consecutive years. Additionally, he has been a Microsoft Certified Trainer for four years, sharing his knowledge and skills with aspiring professionals.

Beyond his professional achievements, Ahmad is a prolific author and reviewer of technical books. His commitment to the tech community is evident in his eight years of organizing IT events and over a decade of enabling and facilitating community and technical initiatives. Ahmad's influence extends internationally, with over 200 sessions and more than 50 workshops delivered at local, European, and global conferences.

Ahmad's expertise, passion, and dedication make him a leading figure in the world of Microsoft technologies and a driving force behind many successful projects and community endeavors.

Table of Contents

Preface

1

Introduction to AI Services

What kinds of things can Generative AI do?

What is Power Platform?

Learning about Power Automate

Learning about Copilots

Learning about Power Apps

Learning about AI Builder technologies

Understanding Power Platform licensing

Exploring additional AI services

Working with Azure AI Services

Working with OpenAI models

Working with services from Google, Anthropic, and more

Summary

2

Configuring an Environment to Support AI Services

Configuring Azure

Requesting API access for Azure OpenAI services

Setting up OpenAI service resources in Azure

Configuring API access for ChatGPT

Configuring your workstation

Configuring Power Platform

Summary

3

Talking to ChatGPT

Working with ChatGPT as a user

So, what’s a token?

Sending prompts to ChatGPT

HTTP

OpenAI GPT-3 completions

Retrieving data from ChatGPT

So, what exactly is JSON?

Working with ChatGPT’s JSON output in Power Automate

Summary

4

Using ChatGPT and Copilot to Create Flows and Apps

Working with Power Automate

Using ChatGPT to create flows

Using ChatGPT Plus to create flows

Using Copilot to create flows

Working with Power Apps

Using ChatGPT to build an app

Using Copilot to build an app

Summary

5

Bootstrapping a Power App with Copilot

Configuring prerequisites

Creating identities in Entra ID

Creating a Dataverse environment

Building a new Power App with Copilot

Configuring the data elements

Creating the model-driven backend app

Creating the canvas frontend app

Enabling automation

Further exploration

Summary

6

Processing Data with Sentiment Analysis

What is sentiment analysis, anyway?

Licensing prerequisites

Configuring solution prerequisites

Creating a shared mailbox

Creating a Microsoft Teams team

Configuring a sentiment analysis flow

Testing the flow

Further exploration

Summary

7

Using Power Automate and AI to Build PowerPoint Presentations

Licensing prerequisites

Learning about the Encodian Flowr connector

Input formatting

Tokens

Populate PowerPoint

Merge Presentations

Interacting with Wikipedia articles

Creating a PowerPoint template

Creating the flow

Creating the Generate Content Summaries scope

Configuring the JSON parameters

Customizing the GPT prompt

Creating the Generate Slides scope

Working with Encodian Flowr

Testing the flow

Further exploration

Summary

8

Building an Event Registration App with Identity Verification

Designing a solution

Licensing prerequisites

Configuring solution prerequisites

Configuring SharePoint Online

Establishing a Teams meeting

Building an input form

Creating flows

Configuring a flow to handle form submission

Processing the identity document

Sending confirmation messages

Testing the flow

Further exploration

Summary

9

Implementing an AI-Enabled Resume Screener

Designing a solution

Licensing prerequisites

Configuring solution prerequisites

Creating a shared mailbox

Creating a team

Configuring SharePoint Online

Configuring the AI model

Enabling the Cloudmersive connector

Creating a flow

Configuring the trigger and variables

Processing the attachment and candidate record

Extracting information from a resume and updating a candidate record

Evaluating the resume with a prompt

Updating the candidate record

Sending confirmation messages

Testing the flow

Further exploration

Summary

10

Crafting an Executive Summary with GPT

Designing a solution

Licensing prerequisites

Configuring solution prerequisites

Enabling subscriptions

Preparing a document template

Setting up a cloud storage provider

Creating the flow

Configuring the trigger

Converting the document

Sending the content to GPT

Populating the document and saving the new file

Testing the flow

Further exploration

Summary

11

Using AI to Tag Images in a SharePoint Library

What is computer vision?

Designing a solution

Licensing prerequisites

Configuring solution prerequisites

Creating a computer vision service

Configuring a SharePoint library

Creating the flow

Configuring the trigger

Working with computer vision

Updating the image details in the library

Testing the flow

Further exploration

Summary

12

Creating a Generative AI-Based Bot

Learning about the solution

What’s a topic, anyway?

What are generative answers?

Designing a solution

Licensing prerequisites

Preparing solution prerequisites

Creating the copilot

Customizing the copilot

Creating a new topic that uses ChatGPT

Disabling template topics

Adding content for generative AI

Testing the copilot

The Holidays topic

Generative answers

Further exploration

Summary

13

Publishing a Generative AI-based Bot

Publishing a bot to Teams

Publishing the bot

Testing the bot

Approving the bot

Publishing a bot to a website

Publishing a bot to Facebook

Publishing a bot to other endpoints

Summary

Index

Other Books You May Enjoy

Preface

Artificial intelligence (AI) is commonly thought of as the capability of a machine or computer system to perform tasks that would normally require human intelligence. Whatever your mental image of AI is—HAL 9000 in 2001: A Space Odyssey, Arnold Schwarzenegger as the Terminator, or Brent Spiner as Data in Star Trek: The Next Generation—it probably represents only one or two facets of the growing capabilities in the field.

Until recently, most AI technologies revolved around machine learning—essentially, an automation of applied statistics. If you’ve forgotten your college statistics classes, it’s basically using large data samples to predict future data (for example, looking at recent home prices to predict future home prices). In the last year and a half, however, generative artificial intelligence (or GenAI, for short) has exploded onto the scene. Generative AI may be one of the most pivotal technologies created in the last hundred years. It’s a watershed moment for both individuals and organizations, potentially redefining what it is to be capable of creativity—or even thought.

In case you’ve been living under a rock, generative AI is the power behind some of the wildest technical cultural phenomena such as OpenAI’s ChatGPT, Midjourney, Dall-E, and Grok (as well as a host of other commercial ventures). Generative AI services can “create” content (based on their training data) that resembles the types of things that people can create. Instead of taking minutes or hours to compose content, many generative AI solutions can return responses in seconds.

Some of the most exciting use cases are around content summarization, reasoning over content in an almost human-like fashion, pattern matching, prediction, and analysis. Generative AI can also mix operating modes or contexts—for example, predicting values based on time-series data and writing a narrative to go along with it.

Throughout this book, we’ll explore some exciting ways to combine the expanding capabilities of different types of AI with Microsoft’s Power Platform tooling (including Power Apps, Power Automate, and Copilot Studio).

Who this book is for

This book is intended for individuals who are interested in exploring and experimenting with AI and automation but who may not have extensive technical experience in the field. This includes those in fields typically categorized as knowledge working, such as business decision-makers, sales representatives, administrative assistants, business development managers, human resources representatives, and business analysts.

The content in this book assumes you have no knowledge of any machine learning or AI concepts (though it certainly helps with understanding some of the more complex topics).

What this book covers

Chapter 1, Introduction to AI Services, introduces some of the basic concepts of AI.

Chapter 2, Configuring an Environment to Support AI Services, walks through the steps necessary to activate subscriptions and enable AI services in your environment.

Chapter 3, Talking to ChatGPT, introduces interacting with ChatGPT.

Chapter 4, Using ChatGPT and Copilot to Create Flows, demonstrates how to use AI to assist in creating Power Automate flows.

Chapter 5, Bootstrapping a Power App with Copilot, focuses on the power of Copilot to help design and modify a basic Power App through a conversational interface.

Chapter 6, Processing Data with Sentiment Analysis, shows how to leverage a machine learning capability to analyze text and then trigger actions based on a positive, negative, or neutral sentiment.

Chapter 7, Using Power Automate and AI to Build PowerPoint Presentations, explores the use of Power Automate and ChatGPT to create a PowerPoint presentation based on content sourced from the internet.

Chapter 8, Building an Event Registration App with Identity Verification, shows how to process government-issued identity documents to verify the identity of a registrant and then send automated meeting confirmations.

Chapter 9, Implementing an AI-Enabled Resume Screener, combines the power of document recognition, entity extraction, and a GPT service to process a submitted resume and evaluate whether it’s a good fit against a particular job description.

Chapter 10, Creating an Executive Summary with GPT, demonstrates how to use GPT to generate an executive summary of a document and then insert it into a document or presentation.

Chapter 11, Using AI to Tag Images in a SharePoint Library, shows how to use the Azure Computer Vision service to categorize images based on content and then update a document library with descriptions and tags.

Chapter 12, Creating a Generative AI-Based Bot, explores the new Copilot Studio interface to create conversational bots that can provide answers through both ChatGPT and by reasoning over a set of document content.

Chapter 13, Publishing a Generative AI-Based Bot, leverages the conversation bot created in Chapter 12 and walks through making the bot available for end users.

To get the most out of this book

To make the most of your studying experience, we recommend the following components:

Azure tenant with free trial subscriptions (https://azure.microsoft.com/en-us/free/ai-services/)OpenAI GPT subscription (https://www.openai.com)Microsoft 365 trial subscription (https://www.microsoft365.com)AI Builder trial capacity (https://learn.microsoft.com/en-us/ai-builder/ai-builder-trials)

Download the example code files

You can download the example code files for this book from GitHub at https://github.com/PacktPublishing/Power-Platform-and-the-AI-Revolution. If there’s an update to the code, it will be updated in the GitHub repository.

We also have other code bundles from our rich catalog of books and videos available at https://github.com/PacktPublishing/. Check them out!

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: “Mount the downloaded WebStorm-10*.dmg disk image file as another disk in your system.”

A block of code is set as follows:

html, body, #map { height: 100%; margin: 0; padding: 0 }

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

[default] exten => s,1,Dial(Zap/1|30) exten => s,2,Voicemail(u100) exten => s,102,Voicemail(b100) exten => i,1,Voicemail(s0)

Any command-line input or output is written as follows:

$ mkdir css $ cd css

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: “Select System info from the Administration panel.”

Tips or important notes

Appear like this.

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1

Introduction to AI Services

In the last few years, the field of artificial intelligence (AI) has undergone remarkable advancements, revolutionizing various domains and reshaping the way we think about and interact with technology. One particularly fascinating branch of AI that has gained significant attention recently is Generative AI. By enabling machines to exhibit creativity (or, more specifically, the appearance of creativity), Generative AI has opened up new frontiers in areas such as art, music, design, and storytelling, in addition to chat and human interaction.

Before we get too ahead of ourselves, let’s talk about some core concepts to help shed some light on how all this works.

What do all these AI terms mean? Generative AI, in particular, refers to a class of algorithms and models that can autonomously generate new and (somewhat) original content. Unlike traditional AI systems, which rely on pre-defined rules or explicit instructions, Generative AI systems are designed to learn from patterns and existing data to produce novel outputs. These systems leverage deep learning techniques, such as generative adversarial networks (GANs), variational autoencoders (VAEs), and recurrent neural networks (RNNs), to emulate the creative processes of the human mind.

As you’ll see, AI has a lexicon all its own. What do we mean when we say things such as generative adversarial networks and variational autoencoders? Let’s make a quick detour and define some of the terms that we’re going to use:

Algorithm: An algorithm is a set of rules (typically expressed in a computer programming language) that are followed when solving problems.Neural network: When we talk about neural networks, we’re talking about computer systems and interactions that are modeled on our understanding of the human brain and nervous system. Like the human brain, the fundamental building blocks of artificial neural networks are referred to as neurons (nodes), each of which connects to other nodes. The connections have concepts of weight and bias, and when inputs reach certain thresholds, they flip on the next node in the chain. Imagine a neural network as layers of nodes arranged in grids, with each node connecting to multiple nodes on the adjacent layer, and each node’s output being used to influence the input in the adjacent layer’s nodes.GAN: A GAN is comprised of two neural networks that compete based on the same source data. GANs can create synthetic data that is unique but imitates the seed data.VAE: A VAE is an algorithm that has two functions. The first takes a complex data structure and then stores a more simplified version of it with some amount of randomness, while the second takes the simplified version and then generates a more complex output. Imagine the encode function as taking a high-resolution picture of a tree, downscaling it (so that it still looks like a tree but is missing some data and possibly looks blurry), and adding a few random pixels to it. When the decode function is activated, it retrieves the simplified data that’s stored and uses it to reconstitute a more high-resolution image of a tree. The new picture looks similar to the original, but partially due to the loss incurred through the simplification of original data and partially due to the insertion of some amount of randomness by the encoder, the new picture is also different.RNN: An RNN is a type of artificial neural network that can process sequential data by preserving information from previous steps.AI model: An AI model is a mathematical algorithm that mimics human intelligence, processing data to make predictions and generate outputs. It learns from training data to perform specific tasks such as image recognition or natural language processing.Large language model: A large language model is a type of AI model that is designed to understand and generate coherent and contextually relevant, human-like text. The popular ChatGPT is an example of a large language model.

There are many more complex concepts (including many more types of neural networks and AI models) behind deep learning and AI systems.

In addition to Generative AI, many types of AI models are currently in use today, such as those designed to do the following:

Estimate shipping routesPredict traffic patterns and congestionFind weather anomaliesIdentify objects in pictures

Each of these different types of models depends on vast quantities of existing data and purpose-built algorithms, combined with training procedures to help the models “learn” how to predict or identify things.

Throughout this book, we’ll be using a variety of AI technologies – from prebuilt, purpose-oriented models to Generative AI. By the time you reach the final examples and exercises, I hope you’ll have some exciting ideas on how you can accelerate your team, organization, or even personal life with AI.

What kinds of things can Generative AI do?

The remarkable power of Generative AI