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

Integrating an endpoint with Power BI to generate predictions

The final step of using Azure ML to train and deploy models is integrating the model. The purpose of training a model is often its consumption. Power BI's integration with Azure ML offers us that consumption without us having to set up complicated HTTP requests through Power Query Editor.

Assuming that you have a trained model in Azure ML and that you have deployed it to a real-time endpoint, you should be able to integrate that model with Power BI. To use that endpoint in Power BI Desktop, you have to sign in with the organizational account that also has access to the endpoint in the Azure ML workspace.

After signing in and importing the data, you can invoke an Azure ML real-time endpoint by using the Azure Machine Learning feature in Power Query Editor to add a new column with predictions.

Let's use the world happiness dataset once again and see it in action:

  1. Open Power BI Desktop.
  2. Import...