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

Chapter 8: Using Your Algorithms and Code

In the previous chapter, you learned how to train and deploy models with built-in frameworks such as scikit-learn or TensorFlow. Thanks to Script Mode, these frameworks make it easy to use your own code, without having to manage any training or deployment containers.

In some cases, your business or technical environment could make it difficult or even impossible to use these containers. Maybe you need to be in full control of how containers are built. Maybe you'd like to implement your own prediction logic. Maybe you're working with a framework or a language that's not natively supported by SageMaker.

In this chapter, you'll learn how to tailor training and prediction containers to your own needs. You'll also learn how to train and deploy your own custom code, using either the SageMaker SDK directly or command-line open source tools. We'll cover the following topics:

  • Understanding how SageMaker invokes...