Category: Amazon Simple Queue Service (SQS)
Fleet managers can use Amazon Web Services (AWS) to ingest and analyze fleet driver data. In this post, we share how a large public transit agency in the United States worked with AWS to create a proof-of-concept (POC) to analyze operator behavior and improve its visibility of sudden acceleration-based events.
To support Singapore’s national vaccination program, the Integrated Health Information Systems (IHiS) needed the capability to scale its systems to sustain significantly higher loads at very short notice. In addition, its teams needed to be able to develop and implement new features at speed to address evolving vaccination policies and changing, on-the-ground requirements. The agency turned to Amazon Web Services (AWS).
Earlier this year, the Australian Bureau of Statistics (ABS) ran the Australian Census, the agency’s most significant workload, on Amazon Web Services (AWS). The Census is the most comprehensive snapshot of the country, and includes around 10 million households and over 25 million people. With the COVID-19 pandemic causing lockdowns across the country, ABS needed a digital option for the Census that was accessible and reliable for millions of people. They turned to the cloud.
Healthcare organization Dr. B launched to get as many COVID-19 vaccines into as many arms as possible. To achieve its mission to make access to care—specifically the COVID-19 vaccine—more efficient and equitable, the company created a serverless solution built on Amazon Web Services (AWS).
AlayaCare, a Canada-based health technology organization founded in 2014, offers a platform for home and community care organizations. The cloud-based platform provides an end-to-end solution for care providers, including back office functionality, client and family portals, remote patient monitoring, and mobile care worker functionality. AlayaCare aims to help care providers by arming them with the technology and data insights they need to deliver personalized care. Using AWS, AlayaCare is building their vision of the future of in-home and virtual care.
Scientists at NC State University’s North Carolina Institute for Climate Studies (NCICS) work with large datasets and complex computational analysis. Traditionally, they did their work using on-premises computational resources. As different projects were stretching the limits of those systems, NCICS decided to explore cloud computing. As part of the Amazon Sustainability Data Initiative, we invited Jessica Mathews, Jared Rennie, and Tom Maycock to share what they learned from using AWS for climate research. As they considered exploring the cloud to support their work, the idea of leaving the comfort of the local environment was a bit scary. And they had questions: How much will it cost? What does it take to deploy processing to the cloud? Will it be faster? Will the results match what they were getting with their own systems? Here is their story and what they learned.
The Intelligence Advanced Research Projects Activity (IARPA) Machine Intelligence from Cortical Networks (MICrONS) program seeks to revolutionize machine learning by better understanding the representations, transformations, and learning rules employed by the brain. We spoke with Dean Kleissas, Research Engineer working on the IARPA MICrONS Project at the Johns Hopkins University Applied Physics Laboratory (JHU/APL), and […]