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

Deploying a model with Amazon Elastic Inference

When deploying a model, you have to decide whether it should run on a CPU instance, or on a GPU instance. In some cases, there isn't much of a debate. For example, some algorithms simply don't benefit from GPU acceleration, so they should be deployed to CPU instances. At the other end of the spectrum, complex deep learning models for Computer Vision or Natural Language Processing run best on GPUs.

In many cases, the situation is not that clear-cut. First, you should know what the maximum predicted latency is for your application. If you're predicting click-through rate for a real-time ad tech application, every millisecond counts. If you're predicting customer churn in a back-office application, not so much.

In addition, even models that could benefit from GPU acceleration may not be large and complex enough to fully utilize the thousands of cores available on a modern GPU. In such scenarios, you're stuck...