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

Chapter 11: Deploying Machine Learning Models

In the previous chapters, we've deployed models in the simplest way possible: by configuring an estimator, calling the fit() API to train the model, and calling the deploy() API to create a real-time endpoint. This is undoubtedly the preferred scenario for development and testing, but it's not the only one.

Models can be imported. For example, you could take an existing model that you trained on your local machine, import it into SageMaker, and deploy it as if you had it trained on SageMaker.

In addition, models can be deployed in different configurations:

  • A single model on a real-time endpoint, which is what we've done so far, as well as several model variants in the same endpoint.
  • A sequence of up to five models, called an inference pipeline.
  • An arbitrary number of related models that are loaded on demand on the same endpoint, known as a multi-model endpoint. We'll examine this configuration in...