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 MLflow

MLflow is an open source platform for machine learning ( It was initiated by Databricks (, who also brought us Spark. MLflow has lots of features, including the ability to deploy Python-trained models on SageMaker.

This section is not intended to be an MLflow tutorial. You can find documentation and examples at

Installing MLflow

Let's set up a virtual environment for MLflow and install all of the required libraries. At the time of writing, the latest version of MLflow is 1.10, and this is the one we'll use here:

  1. We first initialize a new virtual environment on our local machine, named mlflow-example. Then, we activate it:
    $ virtualenv mlflow-example $ source mlflow-example/bin/activate
  2. We install MLflow and the libraries required by our training script:
    $ pip install mlflow gunicorn pandas sklearn xgboost boto3
  3. Finally...