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Automated Machine Learning on AWS

You're reading from   Automated Machine Learning on AWS Fast-track the development of your production-ready machine learning applications the AWS way

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Product type Paperback
Published in Apr 2022
Publisher Packt
ISBN-13 9781801811828
Length 420 pages
Edition 1st Edition
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Author (1):
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Trenton Potgieter Trenton Potgieter
Author Profile Icon Trenton Potgieter
Trenton Potgieter
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Table of Contents (18) Chapters Close

Preface 1. Section 1: Fundamentals of the Automated Machine Learning Process and AutoML on AWS
2. Chapter 1: Getting Started with Automated Machine Learning on AWS FREE CHAPTER 3. Chapter 2: Automating Machine Learning Model Development Using SageMaker Autopilot 4. Chapter 3: Automating Complicated Model Development with AutoGluon 5. Section 2: Automating the Machine Learning Process with Continuous Integration and Continuous Delivery (CI/CD)
6. Chapter 4: Continuous Integration and Continuous Delivery (CI/CD) for Machine Learning 7. Chapter 5: Continuous Deployment of a Production ML Model 8. Section 3: Optimizing a Source Code-Centric Approach to Automated Machine Learning
9. Chapter 6: Automating the Machine Learning Process Using AWS Step Functions 10. Chapter 7: Building the ML Workflow Using AWS Step Functions 11. Section 4: Optimizing a Data-Centric Approach to Automated Machine Learning
12. Chapter 8: Automating the Machine Learning Process Using Apache Airflow 13. Chapter 9: Building the ML Workflow Using Amazon Managed Workflows for Apache Airflow 14. Section 5: Automating the End-to-End Production Application on AWS
15. Chapter 10: An Introduction to the Machine Learning Software Development Life Cycle (MLSDLC) 16. Chapter 11: Continuous Integration, Deployment, and Training for the MLSDLC 17. Other Books You May Enjoy

Chapter 1: Getting Started with Automated Machine Learning on AWS

If you have ever had the pleasure of successfully driving a production-ready Machine Learning (ML) application to completion or you are currently in the process of developing your first ML project, I am sure that you will agree with me when I say, "This is not an easy task!"

Why do I say that? Well, if we ignore the intricacies involved in gathering the right training data, analyzing and understanding that data, and then building and training the best possible model, I am sure you will agree that the ML process in itself is a complicated task process, time-consuming, and entirely manual, making it extremely difficult to automate. And it is these factors, plus many more, that contribute to ML tasks being difficult to automate.

The primary goal of this chapter is to emphasize these challenges by reviewing a practical example that sets the stage for why automating the ML process is difficult. This chapter will highlight what governing factors should be considered when performing this automation and how leveraging various Amazon Web Services (AWS) capabilities can make the task of driving ML projects into production less daunting and fully automated. By the end of this chapter, we will have established a common foundation for overcoming these challenges through automation.

Therefore, in this chapter, we will cover the following topics:

  • Overview of the ML process
  • Complexities in the ML process
  • An example of the end-to-end ML process
  • How AWS can make automating ML development and the deployment process easier
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Automated Machine Learning on AWS
Published in: Apr 2022
Publisher: Packt
ISBN-13: 9781801811828
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