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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

Conventions used

There are a number of text conventions used throughout this book.

Code in text: Indicates code words in text, database table names, folder names, filenames, file extensions, pathnames, dummy URLs, user input, and Twitter handles. Here is an example: "We define a train() function to capture the input parameters and fit an ImagePredictor() to training_data."

A block of code is set as follows:

import boto3 
import sagemaker
aws_region = sagemaker.Session().boto_session.region_name
!sm-docker build --build-arg REGION={aws_region} .

When we wish to draw your attention to a particular part of a code block, the relevant lines or items are set in bold:

import sagemaker
import datetime
image_uri = "<Enter the Image URI from the sm-docker output>"
role = sagemaker.get_execution_role()
session = sagemaker.session.Session()
bucket = session.default_bucket()

Bold: Indicates a new term, an important word, or words that you see onscreen. For instance, words in menus or dialog boxes appear in bold. Here is an example: "Using the Amazon SageMaker management console, click the Open SageMaker Studio button."

Tips or important notes

Appear like this.

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