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

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Training an Image Classification model on ImageNet

In Chapter 5, Training Computer Vision Models, we trained the Image Classification algorithm on a small dataset with dog and cat images (25,000 training images). This time, let's go for something a little bigger.

We're going to train a ResNet-50 network from scratch on the ImageNet dataset, the reference dataset for many computer vision applications (http://www.image-net.org). The 2012 version contains 1,281,167 training images (140 GB) and 50,000 validation images (6.4 GB) from 1,000 classes.

If you want to experiment at a smaller scale, you can work with 5-10% of the dataset. The final accuracy won't be as good, but it doesn't matter for our purposes.

Preparing the ImageNet dataset

This requires a lot of storage: the dataset is 150 GB, so please make sure you have at least 500 GB available to store it in multiple formats. You're also going to need a lot of bandwidth and a lot of patience to...