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

Managing real-time endpoints

SageMaker endpoints serve real-time predictions using models hosted on fully managed infrastructure. They can be created and managed either with the SageMaker SDK, or with an AWS language SDK such as boto3. The latter gives us more flexibility and control. For instance, we can deploy several Production Variants on the same endpoint, and also configure Auto Scaling.

First, let's look at the SageMaker SDK in greater detail.

Managing endpoints with the SageMaker SDK

The SageMaker SDK lets you work with endpoints in several ways:

  • Configure an estimator, train it with fit(), deploy an endpoint with deploy(), and invoke it with predict().
  • Deploy an existing model.
  • Invoke an existing endpoint.
  • Update an existing endpoint.

We've used the first scenario in many examples so far. Let's look at the other ones.

Deploying an existing model

This is useful when you want to import a model that wasn't trained...