AWS Public Sector Blog
Category: HAQM Simple Storage Service (S3)
AWS demonstrates resilient and secure edge-to-cloud at Department of Defense exercise
The US Department of Defense (DoD) increasingly relies on commercial efforts to adopt and integrate novel and emerging technologies that are critical for mission success. The modern defense landscape evolves rapidly, and industry collaboration is a key component of success. AWS has risen to the challenge to fulfill this commercial- and collaborative-focused approach to advancing military innovation. Most recently, AWS did so through its participation in the Technology Readiness Experimentation (T-REX) series of events with partner General Dynamics Information Technology (GDIT),
How to use data from the AWS Open Data program in HAQM Bedrock
Many government agencies, like the National Oceanic and Atmospheric Administration (NOAA), participate in the AWS Open Data Sponsorship Program. In this post, we discuss how to use NOAA datasets in the Registry of Open Data on AWS using HAQM Bedrock Knowledge Bases.
How CIMAR’s platform enables a national lung cancer screening program at scale, powered by AWS
In this post, we describe how AWS Partner CIMAR’s medical image management platform is enabling the rapid roll-out, scaling, and operation of the National Health Service (NHS) England’s national lung cancer program, connecting 124 acute NHS trusts and multiple mobile scanning units.
Leverage generative AI for biocuration using HAQM Bedrock and HAQM Nova foundation models
Personalized therapy for diseases such as cancer utilizes an individual’s unique genomic profile to guide treatment decisions. However, the effect and clinical significance of most genetic variants are uncertain. Accurate classification of the clinical significance of novel genetic variants requires extensive curation of peer-reviewed biomedical literature. In recent years, generative AI has demonstrated promising results in information extraction and text summarization. In this post, we explore how various AWS-native solutions can be used to create a secure, retrieval-augmented, and cost-effective biomedical chatbot designed to facilitate biocuration.
How Fair Trade USA uses AWS to improve working conditions for farmers
Fair Trade USA™ is a nonprofit organization that is committed to eliminating poverty by promoting sustainable development through ethical trade. They work to ensure fair compensation, safe working conditions for farmers and workers, and sustainable farming practices. In this post, you’ll learn how Fair Trade USA leverages HAQM Web Services (AWS) to improve working conditions for farmers and producers around the world.
How AWS can help partners grow in the Middle East
One of the primary strategies for growth for partners is through global expansion. HAQM Web Services (AWS) continues to invest in infrastructure around the globe. The Middle East is one region with huge potential for growth. According to IDC, more than 75 percent of workloads are on premises in the United Arab Emirates (UAE) and Saudi Arabia. The AWS and IDC report “Unlocking the Full Potential of AI in the Middle East” points out that 28 percent of organizations surveyed in the UAE and Saudi Arabia are currently investing in AI while another 50 percent plan to invest. Read this post to learn more.
AWS accelerates digital public infrastructure adoption with automation
In this post, we explore how AWS enables the automation of deploying a DPI-based solution stack. We highlight Sunbird RC (Registry and Credentials) as a real-world digital public goods (DPG) building block. The following example provides an overview and execution of the packaging available in the Sunbird community GitHub repository for provisioning required AWS services and deploying Sunbird RC services.
HAQM EC2 Spot Instances for scientific workflows: Using generative AI to assess availability
In recent years, public sector organizations have found success running their scientific data processing workloads on HAQM Web Services. As the number of workloads increase with the massive data volume and complex scientific simulations, organizations are looking for ways to optimize cost while maintaining research momentum. HAQM EC2 Spot Instances presents a compelling option to run unused HAQM Elastic Compute Cloud (HAQM EC2) capacity with an up to 90 percent discount compared to On-Demand prices. However, the intermittent nature of Spot Instances often requires careful consideration, especially when handling time-sensitive mission-critical workloads. In this post, we discuss how organizations can effectively identify opportunities to use Spot Instances and HAQM Q Business to develop an enhanced Spot Instance analysis.
4 best practices to enhance research IT operations with AWS
Academic research IT departments around the world face the same challenge: how to balance their existing on-premises infrastructure with the opportunities of cloud computing. At the Supercomputing 2024 (SC24) conference, HAQM Web Services hosted a panel featuring two research IT leaders: Circe Tsui, associate director of solutions architecture at Emory University in the Office of Information Technology, and Dr. Robert Shen, director of the RMIT AWS Supercomputing Hub (RACE) at the Royal Melbourne Institute of Technology (RMIT). During the panel, Tsui and Shen shared how their institutions use AWS to augment and enhance their research operations with more scalability, security, and collaboration alongside their on-premises infrastructure. Read this post to learn more.
Self-hosting source code of the Landing Zone Accelerator on AWS
Some customers using HAQM Web Services (AWS) prohibit users from installing software from public sources. Recently, the Landing Zone Accelerator on AWS (LZA) solution added optional capabilities to support this use case. Instead of installing directly from the public LZA GitHub repository, which is the default installation path for most customers, LZA can be self-hosted from your own HAQM Simple Storage Service (HAQM S3) bucket. This post shows the technical steps necessary to install LZA using HAQM S3.