Custom labeling workflows - Amazon SageMaker AI (original) (raw)

These topics help you set up a Ground Truth labeling job that uses a custom labeling template. A custom labeling template allows you to create a custom worker portal UI that workers will use to label data. Template can be created using HTML, CSS, JavaScript, Liquid template language, and Crowd HTML Elements.

Overview

If this is your first time creating a custom labeling workflow in Ground Truth, the following list is a high-level summary of the steps required.

1. Set up your workforce – To create a custom labeling workflow you need a workforce. This topic teaches you about configuring a workforce.
2. Creating a custom template – To create a custom template you must map the data from your input manifest file correctly to the variables in your template.
3. Using optional processing Lambda functions – To control how data from your input manifest is added to your worker template, and how worker annotations are logged in your job's output file.

This topic also has three end-to-end demos to help you better understand how to use custom labeling templates.

Note

The examples in the links below all include pre-annotation and post-annotation Lambda functions. These Lambda functions are optional.

• Demo template: Annotation of images with crowd-bounding-box
• Demo Template: Labeling Intents with crowd-classifier
• Build a custom data labeling workflow with Amazon SageMaker Ground Truth

Topics

• Set up your workforce
• Creating a custom worker task template
• Adding automation with Liquid
• Processing data in a custom labeling workflow with AWS Lambda
• Demo template: Annotation of images with crowd-bounding-box
• Demo Template: Labeling Intents with crowd-classifier
• Create a custom workflow using the API

Label verification and adjustment data in the output manifest

Set up your workforce

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