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

Training and deploying models with built-in algorithms

Amazon SageMaker lets you train and deploy models in many different configurations. Although it encourages best practices, it is a modular service that lets you do things your own way.

In this section, we first look at a typical end-to-end workflow, where we use SageMaker from data upload all the way to model deployment. Then, we discuss alternative workflows, and how you can cherry pick the features that you need. Finally, we will take a look under the hood, and see what happens from an infrastructure perspective when we train and deploy.

Understanding the end-to-end workflow

Let's look at a typical SageMaker workflow. You'll see it again and again in our examples, as well as in the AWS notebooks available on GitHub (https://github.com/awslabs/amazon-sagemaker-examples/):

  1. Make your dataset available in Amazon S3: In most examples, we'll download a dataset from the internet, or load a local copy...