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

Working with more built-in algorithms

In the rest of this chapter, we will run more examples with built-in algorithms, both in supervised and unsupervised mode. This will help you become very familiar with the SageMaker SDK, and learn how to solve actual machine learning problems. The following list shows some of these algorithms:

  • Classification with XGBoost
  • Recommendation with Factorization Machines
  • Dimensionality reduction with PCA
  • Anomaly detection with Random Cut Forest

Classification with XGBoost

Let's train a model on the Boston Housing dataset with the XGBoost algorithm ( As we will see in Chapter 7, Using Built-in Frameworks, SageMaker also supports XGBoost scripts:

  1. We reuse the dataset preparation steps from the previous examples.
  2. We find the name of the XGBoost container. As several versions are supported, we select the latest one (1.0-1 at the time of writing):
    import boto3 from sagemaker import...