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)
1
Section 1: Introduction to Amazon SageMaker
4
Section 2: Building and Training Models
11
Section 3: Diving Deeper on Training
14
Section 4: Managing Models in Production

Preparing image datasets

Input formats are more complex for image datasets than for tabular datasets, and we need to get them exactly right. The CV algorithms in SageMaker support three input formats:

  • Image files
  • RecordIO files
  • Augmented manifests built by SageMaker Ground Truth

In this section, you'll learn how to prepare datasets in these different formats. To the best of my knowledge, this topic has rarely been addressed in such detail. Get ready to learn a lot!

Working with image files

This is the simplest format, and it's supported by all three algorithms. Let's see how to use it with the image classification algorithm.

Converting an image classification dataset to image format

A dataset in image format has to be stored in S3. Images don't need to be sorted in any way, and you simply could store all of them in the same bucket.

Images are described in a list file, a text file containing a line per image. For image classification...