You're reading from Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781800208919

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Julien Simon

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: Introduction to Amazon SageMaker

2. Chapter 1: Introduction to Amazon SageMaker FREE CHAPTER

3. Chapter 2: Handling Data Preparation Techniques

4. Section 2: Building and Training Models

5. Chapter 3: AutoML with Amazon SageMaker Autopilot

6. Chapter 4: Training Machine Learning Models

7. Chapter 5: Training Computer Vision Models

8. Chapter 6: Training Natural Language Processing Models

9. Chapter 7: Extending Machine Learning Services Using Built-In Frameworks

10. Chapter 8: Using Your Algorithms and Code

11. Section 3: Diving Deeper on Training

12. Chapter 9: Scaling Your Training Jobs

13. Chapter 10: Advanced Training Techniques

14. Section 4: Managing Models in Production

15. Chapter 11: Deploying Machine Learning Models

16. Chapter 12: Automating Machine Learning Workflows

17. Chapter 13: Optimizing Prediction Cost and Performance

18. Other Books You May Enjoy

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Building a cost optimization checklist

You should constantly pay attention to cost, even in the early stages of your machine learning project. Even if you're not paying the AWS bill, someone is, and I'm sure quite you'll quickly find out who that person is if you spend too much.

Regularly going through the following checklist will help you spend as little as possible, get the most machine learning happy bang for your buck, and hopefully keep the Finance team off your back!

Optimizing costs for data preparation

With so much focus on optimizing training and deployment, it's easy to overlook data preparation. Yet, this critical piece of the machine learning workflow can incur very significant costs.

Tip #1

Resist the urge to build ad hoc ETL tools running on instance-based services.

Obviously, your workflows will require data to be processed in a custom fashion, for example, to apply domain-specific feature engineering. Working with a managed service...