Book Image

Learn Amazon SageMaker

By : Julien Simon
Book Image

Learn Amazon SageMaker

By: Julien Simon

Overview of this book

Amazon SageMaker enables you to quickly build, train, and deploy machine learning (ML) models at scale, without managing any infrastructure. It helps you focus on the ML problem at hand and deploy high-quality models by removing the heavy lifting typically involved in each step of the ML process. This book is a comprehensive guide for data scientists and ML developers who want to learn the ins and outs of Amazon SageMaker. You’ll understand how to use various modules of SageMaker as a single toolset to solve the challenges faced in ML. As you progress, you’ll cover features such as AutoML, built-in algorithms and frameworks, and the option for writing your own code and algorithms to build ML models. Later, the book will show you how to integrate Amazon SageMaker with popular deep learning libraries such as TensorFlow and PyTorch to increase the capabilities of existing models. You’ll also learn to get the models to production faster with minimum effort and at a lower cost. Finally, you’ll explore how to use Amazon SageMaker Debugger to analyze, detect, and highlight problems to understand the current model state and improve model accuracy. By the end of this Amazon book, you’ll be able to use Amazon SageMaker on the full spectrum of ML workflows, from experimentation, training, and monitoring to scaling, deployment, and automation.
Table of Contents (19 chapters)
Section 1: Introduction to Amazon SageMaker
Section 2: Building and Training Models
Section 3: Diving Deeper on Training
Section 4: Managing Models in Production

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 ( 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...