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

Using the SageMaker training toolkit with scikit-learn

In this example, we're going to build a custom Python container with the SageMaker Training Toolkit. We'll use it to train a scikit-learn model on the Boston Housing dataset, using Script Mode and the SKLearn estimator.

We need three building blocks:

  • The training script: Thanks to Script Mode, we can use exactly the same code as in the Scikit-Learn example from Chapter 7, Extending Machine Learning Services with Built-in Frameworks.
  • We need a Dockerfile and Docker commands to build our custom container.
  • We also need a SKLearn estimator configured to use our custom container.

Let's take care of the container:

  1. A Dockerfile can get quite complicated. No need for that here! We start from the official Python 3.7 image available on Docker Hub ( We install scikit-learn, numpy, pandas, joblib, and the SageMaker training toolkit:
    FROM python:3.7
    RUN pip3 install...