AWS Public Sector Blog
Category: Serverless
Frugal architecture in action: The Urban Institute innovates with R and Serverless on AWS
Nonprofit organizations are typically frugal and responsible. They strive to improve the human condition in innumerable ways, yet they cannot raise capital like a commercial organization, so they have to make the most of the resources they have. They apply that frugal approach to IT: they build and operate only what they need to pursue their mission, and constantly innovate both to meet mission objectives and optimize cost. Even with these constraints, nonprofits aspire to solve some of the world’s biggest problems, and often, they use innovative IT architectures on Amazon Web Services (AWS) to do it.
Concerts for Carers uses AWS Fargate, Amazon Aurora to deliver ticketing and events platform at any scale
In April 2020, during the COVID-19 pandemic, while frontline workers were under immense pressure, three experienced live event professionals wanted to thank all of the UK’s National Health Service (NHS) workers and paid care workers. They combined their extensive experience and knowledge to launch the not-for-profit charity Concerts for Carers, whose mission is to promote the mental health and well-being of all NHS workers and paid caregivers and to provide them with free tickets to live events in the UK as an ongoing gesture of thanks. This post highlights how they’ve used Amazon Web Services to meet their mission.
Satellite mission operations using artificial intelligence on AWS
Cognitive Space is a leading Amazon Web Services (AWS) Partner delivering intelligent automation to satellite constellation operations using the CNTIENT platform. The system uses AWS-powered artificial intelligence (AI) decision making to handle highly complex and dynamic satellite tasking requirements, and demanding mission requirements. This blog post provides technical guidance for building and operating mission operation centers (MOCs) on AWS.
Building the WIS 2.0 global weather cache on AWS
The World Meteorological Organization (WMO) wants to build and modernize a global weather framework with WMO Information Systems (WIS) 2.0 to enable and democratize unified access to critical, up-to-date weather data across the world. The WIS 2.0 system and the global cache provide a single point of access to improve the speed and accuracy with which forecasts can be generated while decreasing the time and capital requirements. This post describes the value of a global weather cache as well as the design and architecture for building the WIS 2.0 global weather cache on Amazon Web Services (AWS).
Use modular architecture for flexible and extensible RAG-based generative AI solutions
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.
4 steps EdTechs can take to grow their profitability on AWS
Funding sources for education technology (EdTech) companies slowed down in 2023. EdTech leaders must strike the right balance of investment to drive growth, and a return on investment for their owners. Read this blog post for an outline of the profitability framework that Amazon Web Services (AWS) uses with customers. The framework helps EdTechs improve cost efficiency, assess functionality expansion, and grow market share.
Northwestern University Libraries make research more efficient, accessible with AWS Lambda
Northwestern University Libraries’ (NUL) relationship with Amazon Web Services (AWS) helped lead to innovative approaches to NUL’s digital collections suite. Read this post to learn how NUL leveraged an open-source standard and AWS Lambda to make it simpler for researchers to examine, compare, share, and cite images and audio/visual files across libraries.
Migrate and modernize public sector applications using containers and serverless
Many public sector customers are interested in building secure, cost-effective, reliable, and highly performant applications. Technologies like containerization and serverless help customers migrate and modernize their applications. In this blog post, learn how public sector customers use offerings from AWS like AWS Lambda, Amazon Elastic Kubernetes Service (Amazon EKS), Amazon Elastic Container Service (Amazon ECS) to build modern applications supporting diverse use cases, including those driven by machine learning (ML) and generative artificial intelligence (AI). If you want to learn more on this topic, please register to attend the webinar series, Build Modern Applications on AWS.
Implement a secure, serverless GraphQL architecture in AWS GovCloud (US) to optimize API flexibility and efficiency
GraphQL is a query language and server-side runtime system for application programming interfaces (APIs) that prioritizes giving clients exactly the information they request and no more. GraphQL can help public sector customers focus on their data and provide ways to explore the data in their APIs. Learn a reference architecture using serverless technologies that you can use to build GraphQL-enabled solutions in the AWS GovCloud (US) Regions to unify data access in real-time and simplify operations.
Creating satellite communications data analytics pipelines with AWS serverless technologies
Satellite communications (satcom) networks typically offer a rich set of performance metrics, such as signal-to-noise ratio (SNR) and bandwidth delivered by remote terminals on land, sea, or air. Customers can use performance metrics to detect network and terminal anomalies and identify trends to impact business outcomes. This walkthrough presents an approach using serverless resources from AWS to build satcom control plane analytics pipelines. The presented architecture transforms the data to extract key performance indicators (KPIs) of interest, renders them in business intelligence tools, and applies machine learning (ML) to flag unexpected SNR deviations.