You're reading from Automated Machine Learning on AWS Fast-track the development of your production-ready machine learning applications the AWS way

Product type Paperback

Published in Apr 2022

Publisher Packt

ISBN-13 9781801811828

Length 420 pages

Edition 1st Edition

Tools

AWS

Concepts

Machine Learning

Author (1):

Trenton Potgieter

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

Technical requirements

You will need access to the Jupyter Notebook environment to follow along with the example in this chapter. Although sample code has been provided for the various steps of the ML process, a Jupyter Notebook example has been provided in this book's GitHub repository (https://github.com/PacktPublishing/Automated-Machine-Learning-on-AWS/blob/main/Chapter01/ML%20Process%20Example.ipynb) for you to work through the entire example at your own pace.

For further instructions on how to set up a Jupyter Notebook environment, you can refer to the installation guide (https://jupyterlab.readthedocs.io/en/stable/getting_started/installation.html) to either set up JupyterLab or classic Jupyter Notebook. Alternatively, for local notebook development using a development IDE, such as Visual Studio Code, you can refer to the VS Code documentation (https://code.visualstudio.com/docs/datascience/jupyter-notebooks).