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

What to look for in your data

The foundation of AI is your data, which is exactly why we need to take very good care of our data. There are two main aspects that we need to investigate: the quantity and the quality of your data. One of the reasons AI is becoming an increasingly popular field is that its models are becoming better and easier to produce. This is partly because of how easy it is to get access to large amounts of data and process those large amounts of data to get a good model, thanks to cloud computing. Garbage in is garbage out, as they say, and the quality of your data is, therefore, just as important. We'll first talk about what we should do around data quantity, and then we'll discuss how we can explore and improve the data quantity.

Understanding data quantity

The purpose of a model is to find a pattern in your data that can be generalized to make interpretations about other or new data points. To make sure the model represents the true data well...