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Azure Data Scientist Associate Certification Guide

You're reading from   Azure Data Scientist Associate Certification Guide A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam

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Product type Paperback
Published in Dec 2021
Publisher Packt
ISBN-13 9781800565005
Length 448 pages
Edition 1st Edition
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Authors (2):
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Andreas Botsikas Andreas Botsikas
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Andreas Botsikas
Michael Hlobil Michael Hlobil
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Michael Hlobil
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Table of Contents (17) Chapters Close

Preface 1. Section 1: Starting your cloud-based data science journey
2. Chapter 1: An Overview of Modern Data Science FREE CHAPTER 3. Chapter 2: Deploying Azure Machine Learning Workspace Resources 4. Chapter 3: Azure Machine Learning Studio Components 5. Chapter 4: Configuring the Workspace 6. Section 2: No code data science experimentation
7. Chapter 5: Letting the Machines Do the Model Training 8. Chapter 6: Visual Model Training and Publishing 9. Section 3: Advanced data science tooling and capabilities
10. Chapter 7: The AzureML Python SDK 11. Chapter 8: Experimenting with Python Code 12. Chapter 9: Optimizing the ML Model 13. Chapter 10: Understanding Model Results 14. Chapter 11: Working with Pipelines 15. Chapter 12: Operationalizing Models with Code 16. Other Books You May Enjoy

Deploying the best model as a web service

In the previous section, you navigated around the run experiment page while reviewing the information related to the run execution and the results of the exploration, which are the trained models. In this section, we will revisit the Models tabs and start deploying the best model as a web service to be able to make real-time inferences. Navigate to the run's details page, as shown in Figure 5.15. Let's get started:

  1. Click on the Models tab. You should see a page similar to the one shown here:

    Figure 5.16 – The Models tab as a starting point for deploying a model

  2. In this list, you can select any model you want to deploy. Select the row with the best model, as shown in the preceding screenshot. Click the Deploy command at the top of the list. The Deploy a model dialog will appear, as shown here:

    Figure 5.17 – The Deploy a model dialogue

  3. In the Deploy a model dialog, you will be able to define a deployment...
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