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)
1
Part 1: AI Fundamentals
5
Part 2: Out-of-the-Box AI Features
13
Part 3: Create Your Own Models

Mitigating bias

It is important to know whether you have a biased dataset, as it may mean you do not have a representative dataset. It may also mean that you will produce an AI model that treats different groups unfairly. For example, your AI model accurately forecasts supermarket sales in cities but underperforms in towns. If you use demand forecasting to plan the supplies to send to each supermarket, this can result in constant supply shortages in your supermarkets that are in towns. (We'll talk about forecasting in Chapter 4, Forecasting Time-Series Data.)

When we talk of bias, we often refer to imbalanced data. To know whether a dataset is imbalanced, we mostly look at histograms, box plots, and the distribution of values. As soon as we see that there is an inequality in the number of values we can find in our dataset, this can mean that there is bias in our data.

Bias is a complex problem and does not have one golden solution. When your data is biased, understanding...