AWS Machine Learning Blog

Category: Amazon Comprehend

Workflow diagram

Moderate audio and text chats using AWS AI services and LLMs

Online gaming and social communities offer voice and text chat functionality for their users to communicate. Although voice and text chat often support friendly banter, it can also lead to problems such as hate speech, cyberbullying, harassment, and scams. Today, many companies rely solely on human moderators to review toxic content. However, verifying violations in […]

Automate PDF pre-labeling for Amazon Comprehend

Amazon Comprehend is a natural-language processing (NLP) service that provides pre-trained and custom APIs to derive insights from textual data. Amazon Comprehend customers can train custom named entity recognition (NER) models to extract entities of interest, such as location, person name, and date, that are unique to their business. To train a custom model, you […]

Use custom metadata created by Amazon Comprehend to intelligently process insurance claims using Amazon Kendra

Structured data, defined as data following a fixed pattern such as information stored in columns within databases, and unstructured data, which lacks a specific form or pattern like text, images, or social media posts, both continue to grow as they are produced and consumed by various organizations. For instance, according to International Data Corporation (IDC), […]

Easily build semantic image search using Amazon Titan

Digital publishers are continuously looking for ways to streamline and automate their media workflows to generate and publish new content as rapidly as they can, but without foregoing quality. Adding images to capture the essence of text can improve the reading experience. Machine learning techniques can help you discover such images. “A striking image is […]

Build well-architected IDP solutions with a custom lens – Part 2: Security

Building a production-ready solution in AWS involves a series of trade-offs between resources, time, customer expectation, and business outcome. The AWS Well-Architected Framework helps you understand the benefits and risks of decisions you make while building workloads on AWS. By using the Framework, you will learn current operational and architectural recommendations for designing and operating […]

Build well-architected IDP solutions with a custom lens – Part 5: Cost optimization

Building a production-ready solution in the cloud involves a series of trade-off between resources, time, customer expectation, and business outcome. The AWS Well-Architected Framework helps you understand the benefits and risks of decisions you make while building workloads on AWS. An intelligent document processing (IDP) project usually combines optical character recognition (OCR) and natural language […]

Build well-architected IDP solutions with a custom lens – Part 6: Sustainability

An intelligent document processing (IDP) project typically combines optical character recognition (OCR) and natural language processing (NLP) to automatically read and understand documents. Customers across all industries run IDP workloads on AWS to deliver business value by automating use cases such as KYC forms, tax documents, invoices, insurance claims, delivery reports, inventory reports, and more. […]

Principal Financial Group uses AWS Post Call Analytics solution to extract omnichannel customer insights

An established financial services firm with over 140 years in business, Principal is a global investment management leader and serves more than 62 million customers around the world. Principal is conducting enterprise-scale near-real-time analytics to deliver a seamless and hyper-personalized omnichannel customer experience on their mission to make financial security accessible for all. They are […]

Flag harmful content using Amazon Comprehend toxicity detection

Online communities are driving user engagement across industries like gaming, social media, ecommerce, dating, and e-learning. Members of these online communities trust platform owners to provide a safe and inclusive environment where they can freely consume content and contribute. Content moderators are often employed to review user-generated content and check that it’s safe and compliant […]

Build trust and safety for generative AI applications with Amazon Comprehend and LangChain

We are witnessing a rapid increase in the adoption of large language models (LLM) that power generative AI applications across industries. LLMs are capable of a variety of tasks, such as generating creative content, answering inquiries via chatbots, generating code, and more. Organizations looking to use LLMs to power their applications are increasingly wary about data privacy to ensure trust and safety is maintained within their generative AI applications. This includes handling customers’ personally identifiable information (PII) data properly. It also includes preventing abusive and unsafe content from being propagated to LLMs and checking that data generated by LLMs follows the same principles. In this post, we discuss new features powered by Amazon Comprehend that enable seamless integration to ensure data privacy, content safety, and prompt safety in new and existing generative AI applications.