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

Deploying a model to an endpoint

To get predictions in Power BI, Power BI needs to send data to the model and get the result back to store it as a new column in the dataset. To accomplish this, the model needs to be deployed to a web service. When you train a model with AutoML, a web service is very easily created. You only need to specify the following:

  • The name of the deployment
  • The compute used to generate predictions: either Azure Container Instances (ACI) for small-scale deployments or Azure Kubernetes Service (AKS) for large-scale deployments

Both ACI and AKS are container orchestration services. ACI is Azure's proprietary service and is easier to use. AKS is based on the open source Kubernetes technology to orchestrate containerized applications. Even though Azure ML can manage and maintain the AKS clusters used for model deployment for you, it is better to use it when you have the expertise to set it up yourself as the management can be quite complex...