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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

Monitoring the pipeline's progress

Monitoring the pipeline execution is done through the CodePipeline console. In the web browser, open the AWS CodePipeline Management Console (https://console.aws.amazon.com/codesuite/codepipeline/home), and then click on the name of the pipeline—abalone-cicd-pipeline. The following screenshot depicts the pipeline execution:

Figure 7.6 – CodePipeline console

If you compare Figure 7.6 with the pipeline in Figure 5.4 of Chapter 5, Continuous Deployment of a Production ML Model, the first thing you will notice is that the Build stage has been significantly compressed to a action called BuildModel. This is because we are offloading the ML modeling process to the Step Functions state machine, instead of capturing the modeling process into the pipeline itself.

To review the progress of the state machine in a new web browser tab, open the AWS Step Functions Management Console (https://console.aws.amazon.com/states...

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