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

Chapter 5: Training Computer Vision Models

In the previous chapter, you learned how to use SageMaker's built-in algorithms for traditional machine learning problems including classification, regression, and anomaly detection. We saw that these algorithms work well on tabular data, such as CSV files. However, they are not well suited for image datasets, and they generally perform very poorly on computer vision (CV) tasks.

For a few years now, CV has taken the world by storm, and not a month goes by without a new breakthrough in extracting patterns from images and videos. In this chapter,you will learn about three built-in algorithms designed specifically for CV tasks.We'll discuss the types of problems that you can solve with them. We'll also spend a lot of time explaining how to prepare image datasets, as this crucial topic is often inexplicably overlooked. Of course, we'll train and deploy models too.

This chapter covers the following topics:

  • Discovering...