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 transparent models

One of the reasons AI has become increasingly popular over the past decade is the development of more complicated models, such as deep learning models. Deep learning models are especially successful on unstructured data as they can derive what features are needed to generate the prediction.

The benefit of deep learning models is that they are often more accurate, while the disadvantage is that they tend not to be very transparent: it is unclear how the model generates a prediction or makes a decision.

Using algorithms that are transparent by design

Transparency is becoming an increasingly important concern when it comes to training machine learning models. Even though more complicated algorithms can be used to train more accurate models, sometimes, a data scientist may opt for a simpler algorithm that is more transparent. The following diagram shows how simpler algorithms, such as linear models and decision trees, have better transparency but may...