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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

Chapter 9: Optimizing the ML Model

In this chapter, you will learn about two techniques you can use to discover the optimal model for your dataset. You will start by exploring the HyperDrive package of the AzureML SDK. This package allows you to fine-tune the model's performance by tweaking the parameters it exposes, a process also known as hyperparameter tuning. You will then explore the Automated ML (AutoML) package of the AzureML SDK, which allows you to automate the model selection, training, and optimization process through code.

In this chapter, we are going to cover the following main topics:

  • Hyperparameter tuning using HyperDrive
  • Running AutoML experiments with code
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