You're reading from Automated Machine Learning Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms

Product type Paperback

Published in Feb 2021

Publisher Packt

ISBN-13 9781800567689

Length 312 pages

Edition 1st Edition

Languages

Python

Tools

Azure Functions

Concepts

Machine Learning

Author (1):

Adnan Masood

View More author details

Table of Contents (15) Chapters

Preface

1. Section 1: Introduction to Automated Machine Learning

2. Chapter 1: A Lap around Automated Machine Learning FREE CHAPTER

3. Chapter 2: Automated Machine Learning, Algorithms, and Techniques

4. Chapter 3: Automated Machine Learning with Open Source Tools and Libraries

5. Section 2: AutoML with Cloud Platforms

6. Chapter 4: Getting Started with Azure Machine Learning

7. Chapter 5: Automated Machine Learning with Microsoft Azure

8. Chapter 6: Machine Learning with AWS

9. Chapter 7: Doing Automated Machine Learning with Amazon SageMaker Autopilot

10. Chapter 8: Machine Learning with Google Cloud Platform

11. Chapter 9: Automated Machine Learning with GCP

12. Section 3: Applied Automated Machine Learning

13. Chapter 10: AutoML in the Enterprise

14. Other Books You May Enjoy

Chapter 6: Machine Learning with AWS

"Whatever you are studying right now, if you are not getting up to speed on deep learning, neural networks, etc., you lose. We are going through the process where software will automate software, automation will automate automation."

– Mark Cuban

In the previous chapter, you were introduced to the Azure Machine Learning (ML) landscape and how to do automated ML in the Azure platform. In this chapter, you will learn how to get started with ML using Amazon Web Services (AWS), along with different offerings and a detailed understanding of the ginormous AWS cloud stack.

The theme of this chapter is to get started with an introduction to AWS ML capabilities to give a wider perspective of this large ecosystem; not only AWS as a hyperscaler but also the breadth of the field itself. Many use cases and permutations require specialized solutions and there is no one-size-fits-all solution for an enterprise's AI and ML needs...