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

Understanding AutoML

Azure ML and AutoML may both be new concepts to you. If you do most of your work in Power BI, you may only use these tools occasionally. Multiple books can be dedicated to either of these concepts, which is why we'll cover the bare necessities for data analysts here.

So, why do we want to learn about AutoML? Throughout this book, we have explored many features and services that offer pretrained models that are ready to use. There is no need to train them, nor to have the data-science expertise to create models from scratch.

Pretrained models are ideal for common scenarios that many organizations face; for example, one model trained to recognize faces can be used for many different applications. However, if you want to have a forecasting model to predict the demand of your products based on your advertisement strategies to plan the supply, a generic model may not be the right fit for you.

It's when you need the model to be trained and tuned...