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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

Summary

In this chapter, we learned in which situations we should use ML and where it is coming from, we understood basic concepts of statistics and the mathematical knowledge we require for ML, and we discovered the steps we need to go through to create a performing ML model. In addition, we had a first glimpse at what is required to operationalize ML projects. This should give a base idea of what ML is about and what we will dive into in this book.

As this book not only covers ML but also the cloud platform Azure, in the next two chapters, we will go deeper into a topic that we have not covered so far—we will speak about tooling for ML. Therefore, in the next chapter, we will discover what Azure has to offer in the form of tools and services for ML, and in the third chapter, we will use the most useful tool to run our first hands-on experimentation with ML on Azure.

You have been reading a chapter from
Mastering Azure Machine Learning - Second Edition
Published in: May 2022
Publisher: Packt
ISBN-13: 9781803232416
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