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

Creating an AutoML experiment in Azure ML

AutoML can be used for training regression, classification, forecasting, or CV models. For the first three, the input dataset is expected to be tabular, but for CV, you'd work with images. To take one task as an example when exploring how to work with AutoML, let's look at forecasting.

In Chapter 4, Forecasting Time-Series Data, we already covered forecasting in Power BI. We learned that Power BI can create a forecast for time-series data based on trends and seasonality that it can find in the target value itself, the target value being the number of tourists in the Netherlands per month from 2012 until 2019. Building on that example, we'll add other features to the dataset to train a forecasting model with AutoML that also takes other information into consideration when forecasting the number of tourists.

The dataset we'll use for AutoML has four columns, as follows:

  • StartMonth: The first day of the month...