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


Open source frameworks such as Scikit-Learn and TensorFlow have made it simple to write machine learning and deep learning code. They've become immensely popular in the developer community and for good reason. However, managing training and deployment infrastructure still requires a lot of effort and skills that data scientists and machine learning engineers typically do not possess. SageMaker simplifies the whole process. You can go quickly from experimentation to production, without ever worrying about infrastructure.

In this chapter, you learned about the different frameworks available in SageMaker for machine learning and deep learning, as well as how to customize their containers. You also learned how to use Script Mode and Local Mode for fast iteration until you're ready to deploy in production. Finally, you ran several examples, including one that combines Apache Spark and SageMaker.

In the next chapter, you will learn how to use your own custom code...