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

Labeling data with Amazon SageMaker Ground Truth

Added to Amazon SageMaker in late 2018, Amazon SageMaker Ground Truth helps you quickly build accurate training datasets. Machine learning practitioners can distribute labeling work to public and private workforces of human labelers. Labelers can be productive immediately, thanks to built-in workflows and graphical interfaces for common image, video, and text tasks. In addition, Ground Truth can enable automatic labeling, a technique that trains a machine learning model able to label data without additional human intervention.

In this section, you'll learn how to use Ground Truth to label images and text.

Using workforces

The first step in using Ground Truth is to create a workforce, a group of workers in charge of labeling data samples.

Let's head out to the SageMaker console: in the left-hand vertical menu, we click on Ground Truth, then on Labeling workforces. Three types of workforces are available: Amazon...