You're reading from Learn Amazon SageMaker A guide to building, training, and deploying machine learning models for developers and data scientists

Product type Paperback

Published in Aug 2020

Publisher Packt

ISBN-13 9781800208919

Length 490 pages

Edition 1st Edition

Languages

Python

Tools

AWS

Concepts

Machine Learning

Author (1):

Julien Simon

View More author details

Table of Contents (19) Chapters

Preface

1. Section 1: Introduction to Amazon SageMaker

2. Chapter 1: Introduction to Amazon SageMaker FREE CHAPTER

3. Chapter 2: Handling Data Preparation Techniques

4. Section 2: Building and Training Models

5. Chapter 3: AutoML with Amazon SageMaker Autopilot

6. Chapter 4: Training Machine Learning Models

7. Chapter 5: Training Computer Vision Models

8. Chapter 6: Training Natural Language Processing Models

9. Chapter 7: Extending Machine Learning Services Using Built-In Frameworks

10. Chapter 8: Using Your Algorithms and Code

11. Section 3: Diving Deeper on Training

12. Chapter 9: Scaling Your Training Jobs

13. Chapter 10: Advanced Training Techniques

14. Section 4: Managing Models in Production

15. Chapter 11: Deploying Machine Learning Models

16. Chapter 12: Automating Machine Learning Workflows

17. Chapter 13: Optimizing Prediction Cost and Performance

18. Other Books You May Enjoy

Leave a review - let other readers know what you think

Discovering 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 Mechanical...