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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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Toc

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

Chapter 6: Visual Model Training and Publishing

Azure Machine Learning (AzureML) Studio offers a designer experience when developing a model by allowing you to drag, drop, and configure training and inference pipelines. In this chapter, you will get an overview of the designer. You will then create a training process. Once you have seen the overall flow that's used with the designer, we will close this chapter by creating an inference pipeline and publishing the trained model artifact as a service endpoint.

In this chapter, we will cover the following topics:

  • Overview of the designer
  • Designing a training process
  • Creating a batch and real-time inference pipeline
  • Deploying a real-time inference pipeline
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