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 9: Scaling Your Training Jobs

In the four previous chapters, you learned how to train models with built-in algorithms, frameworks, or your own code.

In this chapter, you'll learn how to scale training jobs, allowing them to train on larger datasets while keeping the training time and cost under control. We'll start by discussing when and how to take scaling decisions, thanks to monitoring information and simple guidelines. Then, we'll look at pipe mode and distributed training, two key techniques for scaling. We'll also discuss storage alternatives to S3 for large-scale training. Finally, we'll launch a large training job on the ImageNet dataset.

We'll cover the following topics:

  • Understanding when and how to scale
  • Streaming datasets with pipe mode
  • Distributing training jobs
  • Using other storage services
  • Training an image classification model on ImageNet