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

Training and deploying with XGBoost and Sagify

Sagify is a CLI tool that minimizes the amount of work required to train and deploy models on SageMaker ( You write a training function and a prediction function, and Sagify takes care of the rest, both locally and on SageMaker.


At the time of writing, Sagify hasn't been updated for SageMaker SDK v2. If that's still not the case by the time this book is in your hands, please make sure to install SDK v1 in your virtual environment.

Installing Sagify

You only need to run these steps once. If you need more details, you can find them at

  1. We create a virtual environment and activate it:
    $ virtualenv sagify-demo $ source sagify-demo/bin/activate
  2. We install the dependencies:
    $ pip install sagify pandas
  3. We update our local AWS credentials with the SageMaker role in ~/.aws/config. The file should look similar to this: