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

Improving the question answering model

It is very important to monitor and continuously improve a machine learning model. The same goes for the model we created when using the question answering service. Even though it works perfectly when we as designers test it, there is no guarantee that it will stand the test of time.

Over time, new questions may emerge, or users may interact with your app differently. For example, users may ask shorter questions as they get used to working with the question answering service through the app.

There are two main things we can do to improve the model:

  • Use active learning in the Language Studio: An easy approach that analyzes predictions for you. Whenever questions seem too similar, the service concludes you need to provide more information to disambiguate between questions more clearly. Suggestions are provided, and you can review them in the Language Studio in the Review suggestions tab.
  • Log diagnostics in Azure: A more advanced...