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

Examining model artifacts

A model artifact contains one or several files that are produced by a training job that are required for model deployment. The number and the nature of these files depend on the algorithm that was trained. As we've seen many times, the model artifact is stored as a model.tar.gz file, at the S3 output location defined in the estimator.

Let's look at some different examples. You can use the artifacts from the jobs we previously trained for this.

Examining artifacts for built-in algorithms

Most built-in algorithms are implemented with Apache MXNet, and their artifacts reflect this. For more information on MXNet, please visit Let's get started:

  1. Let's start from the artifact for the Linear Learner model we trained in Chapter 4, Training Machine Learning Models:
    $ tar xvfz model.tar.gz x model_algo-1
    $ unzip model_algo-1 archive:  model_algo-1 extracting: additional-params.json extracting...