Book Image

Artificial Intelligence with Power BI

By : Mary-Jo Diepeveen
Book Image

Artificial Intelligence with Power BI

By: Mary-Jo Diepeveen

Overview of this book

The artificial intelligence (AI) capabilities in Power BI enable organizations to quickly and easily gain more intelligent insights from unstructured and structured data. This book will teach you how to make use of the many AI features available today in Power BI to quickly and easily enrich your data and gain better insights into patterns that can be found in your data. You’ll begin by understanding the benefits of AI and how it can be used in Power BI. Next, you’ll focus on exploring and preparing your data for building AI projects and then progress to using prominent AI features already available in Power BI, such as forecasting, anomaly detection, and Q&A. Later chapters will show you how to apply text analytics and computer vision within Power BI reports. This will help you create your own Q&A functionality in Power BI, which allows you to ask FAQs from another knowledge base and then integrate it with PowerApps. Toward the concluding chapters, you’ll be able to create and deploy AutoML models trained in Azure ML and consume them in Power Query Editor. After your models have been trained, you’ll work through principles such as privacy, fairness, and transparency to use AI responsibly. By the end of this book, you’ll have learned when and how to enrich your data with AI using the out-of-the-box AI capabilities in Power BI.

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Table of Contents (18 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Download the example code files
Download the color images
Conventions used
Get in touch
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1
Part 1: AI Fundamentals
Part 1: AI Fundamentals
Free Chapter
2
Chapter 1: Introducing AI in Power BI
Chapter 1: Introducing AI in Power BI
What do we expect from a data analyst?
What is AI?
Why should we use AI in Power BI?
What are our options for AI in Power BI?
Summary
3
Chapter 2: Exploring Data in Power BI
Chapter 2: Exploring Data in Power BI
Technical requirements
What to look for in your data
Using data profiling tools
Using visuals to explore your data
Summary
4
Chapter 3: Data Preparation
Chapter 3: Data Preparation
Fixing the structure of your data
Working with missing data
Mitigating bias
Handling outliers
Summary
5
Part 2: Out-of-the-Box AI Features
Part 2: Out-of-the-Box AI Features
6
Chapter 4: Forecasting Time-Series Data
Chapter 4: Forecasting Time-Series Data
Technical requirements
Data requirements for forecasting
Algorithms used for forecasting
Using forecasting in Power BI
Summary
Further reading
7
Chapter 5: Detecting Anomalies in Your Data Using Power BI
Chapter 5: Detecting Anomalies in Your Data Using Power BI
Technical requirements
Which data is suitable for anomaly detection?
Understanding the logic behind anomaly detection
Using anomaly detection in Power BI
Summary
Further reading
8
Chapter 6: Using Natural Language to Explore Data with the Q&A Visual
Chapter 6: Using Natural Language to Explore Data with the Q&A Visual
Technical requirements
Understanding natural language processing
Creating a Q&A visual in Power BI
Optimizing your Q&A visual
Summary
Further reading
9
Chapter 7: Using Cognitive Services
Chapter 7: Using Cognitive Services
Technical requirements
Understanding Azure's Cognitive Services
Using Cognitive Services for LU
Using Cognitive Services for CV
Summary
10
Chapter 8: Integrating Natural Language Understanding with Power BI
Chapter 8: Integrating Natural Language Understanding with Power BI
Technical requirements
Using Language APIs in Power BI Desktop
Visualizing insights from text in reports
Summary
11
Chapter 9: Integrating an Interactive Question and Answering App into Power BI
Chapter 9: Integrating an Interactive Question and Answering App into Power BI
Technical requirements
Creating a question answering service
Creating an FAQ app with Power Apps
Integrating the FAQ app with Power BI
Improving the question answering model
Summary
12
Chapter 10: Getting Insights from Images with Computer Vision
Chapter 10: Getting Insights from Images with Computer Vision
Technical requirements
Getting insights with Computer Vision using AI Insights
Configuring Custom Vision
Integrating Computer Vision or Custom Vision with Power BI
Using visuals to show a reel of images in a report
Summary
13
Part 3: Create Your Own Models
Part 3: Create Your Own Models
14
Chapter 11: Using Automated Machine Learning with Azure and Power BI
Chapter 11: Using Automated Machine Learning with Azure and Power BI
Technical requirements
Understanding AutoML
Creating an AutoML experiment in Azure ML
Deploying a model to an endpoint
Integrating the model with Power BI
Summary
15
Chapter 12: Training a Model with Azure Machine Learning
Chapter 12: Training a Model with Azure Machine Learning
Technical requirements
Understanding how to train a model
Working with Azure ML
Training a model with Azure ML Designer
Deploying a model for batch or real-time predictions
Integrating an endpoint with Power BI to generate predictions
Summary
16
Chapter 13: Responsible AI
Chapter 13: Responsible AI
Understanding responsible AI
Protecting privacy when using personal data
Creating transparent models
Creating fair models
Summary
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17
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Chapter 1: Introducing AI in Power BI

Everyone wants to be working with data. Organizations are keen on relying more on data-driven decisions instead of intuition-driven decisions. To be driven by data, we need to extract insights from data. Thankfully, Power BI is a great tool to visualize and share what the data tells us. To better understand what trends and learning we can derive from data, we can use techniques from the field of data science.

Data science and artificial intelligence (AI) are becoming increasingly popular approaches to extracting insights from data. Among other things, this is because these tools allow us to work with unstructured data, which we couldn't work with before. And this helps us to more quickly find complicated trends and patterns in the data.

In this book, we will focus on using Microsoft Power BI as a data exploration and visualization tool. And we will take some parts of the Azure cloud to give us the power to train models and integrate this with Power BI.

But first, let's start with some of the groundwork. We need to understand what AI is, to properly scope our projects and run them successfully. We need to know what is possible and how we go from a simple dataset to an AI model before we get into the details of each step of the process. That is why we will first dive into the following questions:

  • What do we expect from a data analyst?
  • What is AI?
  • Why should we use AI in Power BI?
  • What are our options for AI in Power BI?

Let's start with covering the basics.

Previous Section
End of Section 1
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