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

Automating with AWS CloudFormation

AWS CloudFormation has long been the preferred way to automate infrastructure builds and operations on AWS ( We could certainly write a book on the topic, but we'll stick to basics in this section.

The first step in using CloudFormation is to write a template, a JSON or YAML text file describing the resources that you want to build, such as an EC2 instance or an S3 bucket. Resources are available for almost all AWS services, and SageMaker is no exception.If we look at, we see that we can create Notebook instances and deploy endpoints.

A template can (and should) include parameters and outputs. The former help make templates as generic as possible. The latter provide information that can be used by downstream applications, such as instance DNS names or bucket names.

Once you've written your template file, you pass...