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 built-in frameworks

We've covered XGBoost and Scikit-Learn already. Now, it's time to see how we can use deep learning frameworks. Let's start with TensorFlow and Keras.

Working with TensorFlow and Keras

In this example, we're going to train a simple convolutional neural network on the Fashion-MNIST dataset (

Our code is split in two source files: one for the entry point script (, using only TensorFlow 2.x APIs), and one for the model (, based on Keras layers). For the sake of brevity, I will only discuss the SageMaker-related steps. You can find the full code in the GitHub repository for this book:

  1. starts by reading hyperparameters from the command line:
    import tensorflow as tf import numpy as np import argparse, os
    from model import FMNISTModel
    parser = argparse.ArgumentParser()parser.add_argument('--epochs', type=int, default=10)parser.add_argument...