Category: Amazon Machine Learning

In this post, we dive into the architecture and implementation details of GenASL, which uses AWS generative AI capabilities to create human-like ASL avatar videos. GenASL is a solution that translates speech or text into expressive ASL avatar animations, bridging the gap between spoken and written language and sign language.

Through this series of posts, we share our generative AI journey and use cases, detailing the architecture, AWS services used, lessons learned, and the impact of these solutions on our teams and customers. In this first post, we explore Account Summaries, one of our initial production use cases built on Amazon Bedrock. Account Summaries equips our teams to be better prepared for customer engagements. It combines information from various sources into comprehensive, on-demand summaries available in our CRM or proactively delivered based on upcoming meetings. From the period of September 2023 to March 2024, sellers leveraging GenAI Account Summaries saw a 4.9% increase in value of opportunities created.

In this post, we describe the development of the automated summary feature in Verisk’s Discovery Navigator incorporating generative AI, the data, the architecture, and the evaluation of the pipeline. This new functionality offers an immediate overview of the initial injury and current medical status, empowering record reviewers of all skill levels to quickly assess injury severity with the click of a button.

In this post, we show you how to convert Python code that fine-tunes a generative AI model in Amazon Bedrock from local files to a reusable workflow using Amazon SageMaker Pipelines decorators.

Today, we are excited to announce general availability of batch inference for Amazon Bedrock. This new feature enables organizations to process large volumes of data when interacting with foundation models (FMs), addressing a critical need in various industries, including call center operations. In this post, we demonstrate the capabilities of batch inference using call center transcript summarization as an example.

This post explores an innovative application of large language models (LLMs) to automate the process of customer review analysis. LLMs are a type of foundation model (FM) that have been pre-trained on vast amounts of text data. This post discusses how LLMs can be accessed through Amazon Bedrock to build a generative AI solution that automatically summarizes key information, recognizes the customer sentiment, and generates actionable insights from customer reviews. This method shows significant promise in saving human analysts time while producing high-quality results. We examine the approach in detail, provide examples, highlight key benefits and limitations, and discuss future opportunities for more advanced product review summarization through generative AI.

Today, physicians spend about 49% of their workday documenting clinical visits, which impacts physician productivity and patient care. Did you know that for every eight hours that office-based physicians have scheduled with patients, they spend more than five hours in the EHR? As a consequence, healthcare practitioners exhibit a pronounced inclination towards conversational intelligence solutions, […]

Amazon DataZone is a data management service that makes it quick and convenient to catalog, discover, share, and govern data stored in AWS, on-premises, and third-party sources. Amazon DataZone allows you to create and manage data zones, which are virtual data lakes that store and process your data, without the need for extensive coding or […]

This post explores how Accenture used the customization capabilities of Knowledge Bases for Amazon Bedrock to incorporate their data processing workflow and custom logic to create a custom chunking mechanism that enhances the performance of Retrieval Augmented Generation (RAG) and unlock the potential of your PDF data.

QnABot on AWS (an AWS Solution) now provides access to Amazon Bedrock foundational models (FMs) and Knowledge Bases for Amazon Bedrock, a fully managed end-to-end Retrieval Augmented Generation (RAG) workflow. You can now provide contextual information from your private data sources that can be used to create rich, contextual, conversational experiences. In this post, we discuss how to use QnABot on AWS to deploy a fully functional chatbot integrated with other AWS services, and delight your customers with human agent like conversational experiences.