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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

Interacting with the Azure ML resource

In the previous chapter, you deployed the packt-learning-mlw machine learning resource within the packt-azureml-rg resource group. Navigate to the deployed resource by typing in its name in the top search bar and selecting the resource from the results list:

Figure 3.1 – Navigating to the Azure Machine Learning resource

This will land you on the overview pane of the resource, as shown in Figure 3.2:

  1. On the left-hand side, you will see the typical resource menu that most of the Azure services have. This menu is also referred to as the left pane.
  2. At the top, you will see the command bar, which allows you to download the config.json file, a file that contains all the information you need to connect to the workspace through the Python SDK, and to delete the machine learning workspace.
  3. Below the command bar, you can see the working pane, which is where you can view information related to the workspace...
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