To train a machine learning (ML) model, you need a large, high-quality, labeled dataset. Amazon SageMaker Ground Truth helps you build high-quality training datasets for your ML models. With Ground Truth, you can use workers from either Amazon Mechanical Turk, a vendor company of your choosing, or an internal, private workforce to enable you to create a labeled dataset. You can use the labeled dataset output from Ground Truth to train your own models. You can also use the output as a training dataset for an Amazon SageMaker model.

Documents are a primary tool for record keeping, communication, collaboration, and transactions across many industries, including financial, medical, legal, and real estate. For example, millions of mortgage applications and hundreds of millions of tax forms are processed each year. Documents are often unstructured, which means the content's location or format may vary between two otherwise similar forms. Unstructured documents require time-consuming and complex processes to enable search and discovery, business process automation, and compliance control. When using machine learning (ML) to automate processing of these unstructured documents, you can now build in human reviews to aid in managing sensitive workflows that require human judgment.