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

Getting insights with Computer Vision using AI Insights

The easiest way to analyze images in Power BI is by using the Computer Vision model provided to us through the AI Insights feature in Power BI. The AI Insights feature in Power BI does exactly the same as the Computer Vision API from Azure's Cognitive Services.

When we use a pretrained Computer Vision model such as the one offered by Cognitive Services and AI Insights, we don't have to provide any training dataset and can use the model to analyze our images.

Using a pretrained model brings with it advantages and disadvantages. As already discussed in Chapter 7, Using Cognitive Services, the purpose of these pretrained models is to reduce the time and expertise needed by someone such as you to apply them. However, pretrained models are often trained on a generic dataset, which makes them applicable to many different situations.

It may be of course that for your specific use case, using a pretrained model doesn...