Some deep learning and machine learning applications need to ensure accuracy through human oversight of sensitive data. This provides the collection of health data, increases the model accuracy, and helps continuous improvements with updated predictions. Data augmentation is a critical process for data companies that spend tons of dollars on this. Today, we will create a sentiment analysis workflow on Amazon Human Review Workflow, an Amazon A2I service.

AWS is excited to announce the general availability of Amazon Augmented AI (Amazon A2I), a new service that makes it easy to implement human reviews of machine learning (ML) predictions at scale. Amazon A2I removes the undifferentiated heavy lifting associated with building and managing expensive and complex human review systems, so you can ensure your ML models produce accurate predictions. Amazon A2I enables humans and machines to do what they do best by easily inserting human judgment into the ML pipeline. Amazon A2I provides built-in human review workflows for common ML tasks such as content moderation and text extraction from documents, in combination with Amazon Rekognition and Amazon Textract. You can also create your own human review workflows for ML models built with Amazon SageMaker or with any on-premises or cloud tools via its API.

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.