# Custom labeling workflows
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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](https://shopify.github.io/liquid/), and [Crowd HTML Elements](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-ui-template-reference.html).

## Overview
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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.

1. *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.

1. *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`](sms-custom-templates-step2-demo1.md)
+ [Demo Template: Labeling Intents with `crowd-classifier`](sms-custom-templates-step2-demo2.md)
+ [Build a custom data labeling workflow with Amazon SageMaker Ground Truth](https://aws.amazon.com/blogs/machine-learning/build-a-custom-data-labeling-workflow-with-amazon-sagemaker-ground-truth/)

**Topics**
+ [Overview](#sms-custom-templates-overview)
+ [Set up your workforce](sms-custom-templates-step1.md)
+ [Creating a custom worker task template](sms-custom-templates-step2.md)
+ [Adding automation with Liquid](sms-custom-templates-step2-automate.md)
+ [Processing data in a custom labeling workflow with AWS Lambda](sms-custom-templates-step3.md)
+ [Demo template: Annotation of images with `crowd-bounding-box`](sms-custom-templates-step2-demo1.md)
+ [Demo Template: Labeling Intents with `crowd-classifier`](sms-custom-templates-step2-demo2.md)
+ [Create a custom workflow using the API](sms-custom-templates-step4.md)