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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

Overview of SageMaker Autopilot

SageMaker Autopilot is the AWS service that provides AutoML functionality to its customers. Autopilot addresses the various requirements for AutoML by piecing together the following SageMaker modules into an automated framework:

  • SageMaker Processing: Processing jobs take care of the heavy lifting and scaling requirements of organizing, validating, and feature engineering the data, all using a simplified and managed experience.
  • SageMaker Built-in Algorithms: SageMaker helps ML practitioners to get started with model-building tasks by providing several pre-built algorithms that cater to multiple use case types.
  • SageMaker Training: Training jobs take care of the heavy lifting and scaling tasks associated with provisioning the required compute resources to train the model.
  • Automatic Model Tuning: Model tuning or hyperparameter tuning scales the model tuning task by allowing the ML practitioner to execute multiple training jobs, each...
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