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.
Table of Contents (18 chapters)
Part 1: AI Fundamentals
Part 2: Out-of-the-Box AI Features
Part 3: Create Your Own Models


In this chapter, we have worked on the exploration of structured, semi-structured, and unstructured data in the Power Query Editor. We have imported text and images and have made sure that Power BI knows how to interpret this data so that we can later use AI features to extract insights from this rich data. We have looked at the three common problems with data when it is used for AI: missing data, bias, and outliers. We have discussed the different considerations for each of these problems where it is important to understand what causes them and whether they will cause problems for what you want to do with your data. We also covered how we can fix missing data, bias, and outliers, if needed, to make sure we have a representative dataset that will result in building accurate models.

In the next chapter, we will discover the first type of model we can work with in Power BI: forecasting.