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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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Toc

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

Using AutoGluon for tabular data

In the previous chapter, we used Autopilot to see an example AutoML experiment that applies to the ACME Fishing Logistics use case. In this example, we are going to reproduce this experiment with AutoGluon. So, let's see how we can use AutoGluon to automate this task.

Note

The AutoGluon Tabular library benefits from compute instances with as much memory as possible. It is, therefore, recommended that AWS M5 instances (https://aws.amazon.com/ec2/instance-types/m5/) are used for tabular experiments. We will be using an m5.xlarge instance in this example and, therefore, running the example will incur AWS resource costs.

Prerequisites

Before we begin, there are a few fundamental topics that need to be accounted for, namely:

  • At the time of writing, the AutoGluon library is not natively included as one of SageMaker's built-in estimators. This means that we will have to create our own Docker container for AutoGluon, using the...
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