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Mastering Azure Machine Learning

You're reading from   Mastering Azure Machine Learning Execute large-scale end-to-end machine learning with Azure

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Product type Paperback
Published in May 2022
Publisher Packt
ISBN-13 9781803232416
Length 624 pages
Edition 2nd Edition
Tools
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Authors (2):
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Marcel Alsdorf Marcel Alsdorf
Author Profile Icon Marcel Alsdorf
Marcel Alsdorf
Christoph Körner Christoph Körner
Author Profile Icon Christoph Körner
Christoph Körner
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Toc

Table of Contents (23) Chapters Close

Preface 1. Section 1: Introduction to Azure Machine Learning
2. Chapter 1: Understanding the End-to-End Machine Learning Process FREE CHAPTER 3. Chapter 2: Choosing the Right Machine Learning Service in Azure 4. Chapter 3: Preparing the Azure Machine Learning Workspace 5. Section 2: Data Ingestion, Preparation, Feature Engineering, and Pipelining
6. Chapter 4: Ingesting Data and Managing Datasets 7. Chapter 5: Performing Data Analysis and Visualization 8. Chapter 6: Feature Engineering and Labeling 9. Chapter 7: Advanced Feature Extraction with NLP 10. Chapter 8: Azure Machine Learning Pipelines 11. Section 3: The Training and Optimization of Machine Learning Models
12. Chapter 9: Building ML Models Using Azure Machine Learning 13. Chapter 10: Training Deep Neural Networks on Azure 14. Chapter 11: Hyperparameter Tuning and Automated Machine Learning 15. Chapter 12: Distributed Machine Learning on Azure 16. Chapter 13: Building a Recommendation Engine in Azure 17. Section 4: Machine Learning Model Deployment and Operations
18. Chapter 14: Model Deployment, Endpoints, and Operations 19. Chapter 15: Model Interoperability, Hardware Optimization, and Integrations 20. Chapter 16: Bringing Models into Production with MLOps 21. Chapter 17: Preparing for a Successful ML Journey 22. Other Books You May Enjoy

Exploring the Azure Machine Learning service

Before we continue to set up our own development environment and do some ML, we will have a look at what was just deployed besides the main workspace, get a base understanding of all features available in the service, which we will utilize throughout the book, and have a first short look at Azure Machine Learning Studio.

Analyzing the deployed services

We will start by navigating to the Azure portal again. There, type the name of the workspace as mldemows in the top search bar. You should see something like the result shown in Figure 3.3:

Figure 3.3 – An Azure portal search for an ML workspace

As you can see, besides the main mldemows workspace, three other services were deployed, namely Storage account, Key vault, and Application Insights. As most of them require unique names, you will see a random alphanumeric code at the end of each name. For each one of these additional services, we can provide our...

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