# Data labeling with a human-in-the-loop
<a name="data-label"></a>

To train a machine learning model, you need a large, high-quality, labeled dataset. You can label your data using Amazon SageMaker Ground Truth. Choose from one of the Ground Truth [built-in task types](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html) or create your own [custom labeling workflow](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-custom-templates.html). To improve the accuracy of your data labels and reduce the total cost of labeling your data, use Ground Truth enhanced data labeling features like [automated data labeling](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-automated-labeling.html) and [annotation consolidation](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-annotation-consolidation.html). 



**Topics**
+ [Training data labeling using humans with Amazon SageMaker Ground Truth](sms.md)
+ [Use Amazon SageMaker Ground Truth Plus to Label Data](gtp.md)
+ [Workforces](sms-workforce-management.md)
+ [Crowd HTML Elements Reference](sms-ui-template-reference.md)
+ [Using Amazon Augmented AI for Human Review](a2i-use-augmented-ai-a2i-human-review-loops.md)