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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

Executing the automated ML model deployment

Reviewing the pipeline execution is done through the CodePipeline console (https://console.aws.amazon.com/codesuite/codepipeline/home), and then by clicking on the abalone-cicd-pipeline name. Once the pipeline dashboard opens, the first thing you will immediately see (as shown in Figure 5.3) is that the Source stage has succeeded.

Figure 5.3 – Succeeded Source stage

Once the Source stage succeeds, the pipeline automatically moves onto the Build stage to essentially execute the continuous integration phase of the CI/CD process. Figure 5.4 shows the four stage actions that cover continuous integration.

Figure 5.4 – Continuous integration phase of the pipeline

From Figure 5.4, there are a few items to take note of. Firstly, note the Pipeline execution ID. This ID is used to track the lineage of the ML model's release, as it is embedded into each the name of the various SageMaker...

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