Book Image

Learn Amazon SageMaker - Second Edition

By : Julien Simon
Book Image

Learn Amazon SageMaker - Second Edition

By: Julien Simon

Overview of this book

Amazon SageMaker enables you to quickly build, train, and deploy machine learning models at scale without managing any infrastructure. It helps you focus on the machine learning problem at hand and deploy high-quality models by eliminating the heavy lifting typically involved in each step of the ML process. This second edition will help data scientists and ML developers to explore new features such as SageMaker Data Wrangler, Pipelines, Clarify, Feature Store, and much more. You'll start by learning how to use various capabilities of SageMaker as a single toolset to solve ML challenges and progress to cover features such as AutoML, built-in algorithms and frameworks, and writing your own code and algorithms to build ML models. The book will then show you how to integrate Amazon SageMaker with popular deep learning libraries, such as TensorFlow and PyTorch, to extend the capabilities of existing models. You'll also see how automating your workflows can help you get to production faster with minimum effort and at a lower cost. Finally, you'll explore SageMaker Debugger and SageMaker Model Monitor to detect quality issues in training and production. 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)
1
Section 1: Introduction to Amazon SageMaker
4
Section 2: Building and Training Models
11
Section 3: Diving Deeper into Training
14
Section 4: Managing Models in Production

Transforming data with Amazon SageMaker Data Wrangler

Collecting and labeling data samples is only the first step in preparing a dataset. Indeed, it's very likely that you'll have to pre-process your dataset in order to do the following, for example:

  • Convert it to the input format expected by the machine learning algorithm you're using.
  • Rescale or normalize numerical features.
  • Engineer higher-level features, for example, one-hot encoding.
  • Clean and tokenize text for natural language processing applications

In the early stage of a machine learning project, it's not always obvious which transformations are required, or which ones are most efficient. Thus, practioners often need to experiment with lots of different combinations, transforming data in many different ways, training models, and evaluating results.

In this section, we're going to learn about Amazon SageMaker Data Wrangler, a graphical interface integrated in SageMaker...