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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
Tools
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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

Configuring the MWAA prerequisites

Before we can launch the MWAA service, there are a few prerequisites that need to be addressed, namely:

  • MWAA requires access to an S3 bucket where the DAGs are stored.
  • MWAA needs to access a requirements.txt file, also stored on S3, to load any unique Python libraries that the workers would need to execute their assigned tasks.
  • Although not required by MWAA, we need to also configure various IAM roles to access backend services such as Glue and SageMaker.
  • We also need to provide the artifacts that the various backend services would require. For example, we need to provide ETL scripts in order for the Glue service to execute.

In the following steps, we will provide these prerequisites as a CDK application:

  1. Log in to the same AWS account you've been using in the previous chapter and open the AWS Cloud9 console (https://console.aws.amazon.com/cloud9).
  2. In the Your environments section, click the Open IDE button...
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