Category: Amazon API Gateway

Citizens are increasingly expecting government to provide modern digital experiences for conducting online transactions. Market research tells us 63 percent of consumers see personalization as the standard level of service. This post offers various architectural patterns for improving customer experience for the public sector for a wide range of use cases. The aim of the post is to help public sector organizations create customer experience solutions on the Amazon Web Services (AWS) Cloud using AWS artificial intelligence (AI) services and AWS purpose-built data analytics services.

In this post, we will explore the use of generative artificial intelligence (AI) chatbots as a natural language alternative to the service catalog approach. We will present an Amazon Web Services (AWS) architecture pattern to deploy an AI chatbot that can understand user requests in natural language and provide interactive responses to user requests, directing them to the specific systems or services they are looking for. Chatbots simplify the content navigation and discovery process while improving the customer experience.

In this post, we cover an Amazon Web Services (AWS) Cloud infrastructure with a modular architecture that enables you to explore and take advantage of the benefits from different Retrieval-Augmented Generation (RAG)-based generative AI resources in a flexible way. This solution provides several benefits, along with faster time-to-market and shorter development cycles.

Within the Food and Drug Administration’s Center for Drug Evaluation and Research, the Division of Medication Error Prevention and Analysis (DMEPA) plays a critical role. DMEPA reviews premarket and postmarket drug labeling to minimize the risk of medication errors. In partnership with DMEPA, Amazon Web Services (AWS) Partner ICF is developing a machine learning (ML) prototype known as the Computerized Labeling Assessment Tool (CLAT). The prototype employs innovative applications of optical character recognition (OCR) technology and the novel use of computer vision techniques that will alleviate bottlenecks in and enhance the efficiency of the drug labeling review process.

Government agencies are increasingly using large language models (LLMs) powered by generative artificial intelligence (AI) to extract valuable insights from their data in the Amazon Web Services (AWS) GovCloud (US) Regions. In this guide, we walk you through the process of hosting LLMs on Amazon Elastic Compute Cloud (Amazon EC2) instances, using the Hugging Face Text Generation Inference (TGI) Container (TGI) for serving custom LLMs.

Wake Forest is a leading private university in the US with close to 9,000 enrolled students and almost 7,000 faculty and staff. With more than 82,000 degree recipients across all 50 US states and 103 foreign countries, its broader community is vast and growing. To make the most of this diverse community, university leaders want to create connections between individuals across graduating classes, disciplines, and geographies. This post highlights how the school used Amazon Web Services (AWS) to build a solution that brings its whole community closer. 

The University of Pittsburgh, the University of Illinois Urbana-Champaign (UIUC), the University of California Davis Health Cloud Innovation Center (UCDH CIC)—powered by Amazon Web Services (AWS)—and the AWS Digital Innovation (DI) team have built a prototype that uses machine learning (ML) and generative artificial intelligence (AI) to transform the public health communications landscape by giving officials the tools they need to fight medical misinformation, disinformation, and malinformation.

The Pacific Northwest National Laboratory (PNNL) developed Aether as a reusable framework for sharing data and analytics with sponsors and stakeholders. Aether is a mature cloud-centered framework designed using Amazon Web Services (AWS) serverless services to provide a cost-effective and reliable environment for a dozen projects currently deployed with the framework. Read this post to learn more about how Aether’s serverless-first approach is enabling disaster response and risk management.

Climate risk consists of transition risk and physical risk. Transition risk represents regulatory and market-based risks while physical climate risk covers climate-related earth processes and its effects on the built and natural environment. In this blog post, we highlight how to use Amazon Web Services (AWS) to enrich your asset portfolio with open climate data hosted in AWS.

More than 180 courts compose the Nebraska Judicial Branch, which together handle more than 285,000 cases annually and all of the case exhibits that come with such a workload. This blog post highlights the Judicial Branch’s journey to building an electronic exhibits system on Amazon Web Services (AWS).