Category: Amazon Simple Storage Service (S3)

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.

In this post, you will learn how to deploy Amazon Web Services (AWS) Cloud Intelligence Dashboards (CID) using the Landing Zone Accelerator on AWS (LZA) solution. In doing so, you will learn how to customize your LZA deployment using the customizations-config.yaml file. By utilizing the LZA and CID together, you can streamline the deployment process, ensure compliance with best practices, and gain valuable insights into your cloud environment, ultimately leading to improved operational efficiency, enhanced security, and better-informed decision-making.

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.

Kokomo24/7 is a health and safety software education technology (EdTech) platform committed to creating safer schools and communities. Kokomo used Amazon Web Services (AWS) database and analytics tools to create a health-tracking platform that allowed the Los Angeles Unified School District (LAUSD), the second-largest school district in the US, to cut its costs by 50 percent while improving flexibility and response times.

The International University of Applied Sciences (IU) maintains 90 percent of its course content online. Through its online programs, IU aims to give people worldwide access to highly individualized education, enabling them to further enrich their lives. The large majority of IU’s infrastructure runs on Amazon Web Services (AWS). Read this post to learn why IU worked directly with AWS experts through the Experience-Based Acceleration (EBA) program to expand their automated video generation pipelines to be more scalable, modular, and robust.

This post discusses how Amazon Web Services (AWS) can help you successfully set up an Amazon DataZone domain, aggregate data from multiple sources into a single centralized environment, and perform analytics on that data. Additionally, this post provides a sample architecture as well as a walkthrough on how to set up that architecture. Ultimately, this post serves as a valuable resource if you’re seeking to optimize your data management processes and derive actionable insights to drive business growth.

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.

ASTERRA, an Israel-based geospatial and Earth observation company, uses Amazon Web Services (AWS) to help water utilities and a number of industries identify and mitigate pipeline leaks. ASTERRA uses AWS to derive intelligence and insights from beneath the surface of their largest installations and to bypass the need to break ground and dig for leaks. Read this post to learn how AWS has helped ASTERRA overcome traditional on-premises infrastructure limitations and to accelerate the development of solutions for anticipating and mitigating failures, saving water, energy, and avoiding carbon dioxide (CO2) emissions as a result.

In 2022, Emory launched the AI.Humanity initiative to explore the societal impacts of artificial intelligence (AI) and influence its future development to serve humanity. Emory aims to be a leading advocate for ethical use of AI and a top destination for students and faculty seeking to understand and apply its transformative technologies. Read this blog post to learn how Emory uses Amazon Web Services (AWS) to support the computing needs of AI.Humanity.

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.