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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

Chapter 1: Understanding the End-to-End Machine Learning Process

Welcome to the second edition of Mastering Azure Machine Learning. In this first chapter, we want to give you an understanding of what kinds of problems require the use of machine learning (ML), how the full ML process unfolds, and what knowledge is required to navigate this vast terrain. You can view it as an introduction to ML and an overview of the book itself, where for most topics we will provide you with a reference to upcoming chapters so that you can easily find your way around the book.

In the first section, we will ask ourselves what ML is, when we should use it, and where it comes from. In addition, we will reflect on how ML is just another form of programming.

In the second section, we will lay the mathematical groundwork you require to process data, and we will understand that the data you work with probably cannot be fully trusted. Further, we will look at different classes of ML algorithms, how they are defined, and how we can define the performance of a trained model.

Finally, in the third section, we will have a look at the end-to-end process of an ML project. We will understand where to get data from, how to preprocess data, how to choose a fitting model, and how to deploy this model into production environments. This will also get us into the topic of ML operations, known as MLOps.

In this chapter, we will cover the following topics:

  • Grasping the idea behind ML
  • Understanding the mathematical basis for statistical analysis and ML modeling
  • Discovering the end-to-end ML process
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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