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Artificial Intelligence with Power BI

You're reading from   Artificial Intelligence with Power BI Take your data analytics skills to the next level by leveraging the AI capabilities in Power BI

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801814638
Length 348 pages
Edition 1st Edition
Languages
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Author (1):
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Mary-Jo Diepeveen Mary-Jo Diepeveen
Author Profile Icon Mary-Jo Diepeveen
Mary-Jo Diepeveen
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Table of Contents (18) Chapters Close

Preface 1. Part 1: AI Fundamentals
2. Chapter 1: Introducing AI in Power BI FREE CHAPTER 3. Chapter 2: Exploring Data in Power BI 4. Chapter 3: Data Preparation 5. Part 2: Out-of-the-Box AI Features
6. Chapter 4: Forecasting Time-Series Data 7. Chapter 5: Detecting Anomalies in Your Data Using Power BI 8. Chapter 6: Using Natural Language to Explore Data with the Q&A Visual 9. Chapter 7: Using Cognitive Services 10. Chapter 8: Integrating Natural Language Understanding with Power BI 11. Chapter 9: Integrating an Interactive Question and Answering App into Power BI 12. Chapter 10: Getting Insights from Images with Computer Vision 13. Part 3: Create Your Own Models
14. Chapter 11: Using Automated Machine Learning with Azure and Power BI 15. Chapter 12: Training a Model with Azure Machine Learning 16. Chapter 13: Responsible AI 17. Other Books You May Enjoy

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...

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