AWS Public Sector Blog
Tag: machine learning on AWS
Using AI for intelligent document processing to support benefit applications and more
Each year, US federal, state, and local government agencies spend a significant part of their budgets on various social and safety net programs. Tens of millions of residents apply for these benefits every year. In these applications, documents—in various sources, formats, and layouts—are the primary tools for application assessment. Artificial intelligence (AI) technology can accelerate and simplify the application review process, improving both the case worker and applicant experience. Learn how public sector agencies can leverage AI offerings from AWS, like Amazon Textract and Amazon Comprehend, to process multiple documents in benefit application use cases in an intelligent document processing (IDP) workflow.
Large scale AI in digital pathology without the heavy lifting
Pathology is currently undergoing a transformation. While microscopes still dominate many workflows, digital pathology combined with artificial intelligence (AI) is disrupting the space. AI tools can complement expert assessment with quantitative measurements to enable data-driven medicine. Ultivue is a healthcare technology (HealthTech) company that provides high-quality multiplex immunofluorescence assays and large-scale, AI-based computational pathology—built on AWS.
AMILI helps advance precision medicine by building microbiome library on AWS
AMILI is a healthcare technology (HealthTech) company based in Singapore that seeks to advance precision medicine and personalized health and nutrition by harnessing the potential of the microbiome. AMILI uses artificial intelligence (AI) and machine learning (ML) on AWS to comprehensively quantify and characterize gut microbiomes. AMILI aims to build and curate the world’s largest multi-ethnic Asia microbiome database.
5 public sector technology predictions for 2023
As we begin the new year, the AWS worldwide public sector (WWPS) team wanted to share a few of our predictions for public sector technology in 2023. We hope these predictions will help guide and inspire you as you continue your digital transformation journey this year.
Understanding wildfire risk in a changing climate with open data and AWS
The First Street Foundation, a nonprofit research and technology group, is committed to making climate risk information accessible, simple to understand, and actionable for individuals, governments, and industry. As part of the Amazon Sustainability Data Initiative (ASDI), AWS invited Dr. Ed Kearns, the chief data officer of First Street Foundation, to share how AWS technologies and open data are supporting their mission to provide accurate and up-to-date information on climate related risks.
Supporting smart and sustainable transportation innovation at the ITS World Congress
This year, the Intelligent Transportation Society (ITS) World Congress brought together more than 6,000 industry professionals from 64 countries to discuss ways to advance the deployment of intelligent transportation technologies to save lives, improve mobility, promote sustainability, and increase access to communities across the globe. For the event, ITS organized a Global Innovation Competition sponsored by AWS to encourage the development of innovative solutions that address priority themes using cloud technology. Learn more about the winners and how they’re using AWS to transform transportation.
How nonprofits reimagine work using smart technology
Nonprofit leaders today have various technical products and solutions to consider. The addition of “smart technology” to your nonprofit’s technology conversations may seem intimidating or even unfamiliar to the human-centered work that your organization does. But smart technology can help make your nonprofit’s work more human – automating burdensome tasks for your teams and directing their creativity and bandwidth to what really matters: your mission. Learn how nonprofits can use AWS to develop smart tech to innovate for their communities.
Predicting diabetic patient readmission using multi-model training on Amazon SageMaker Pipelines
Diabetes is a major chronic disease that often results in hospital readmissions due to multiple factors. An estimated $25 billion is spent on preventable hospital readmissions that result from medical errors and complications, poor discharge procedures, and lack of integrated follow-up care. If hospitals can predict diabetic patient readmission, medical practitioners can provide additional and personalized care to their patients to pre-empt this possible readmission, thus possibly saving cost, time, and human life. In this blog post, learn how to use machine learning (ML) from AWS to create a solution that can predict hospital readmission – in this case, of diabetic patients – based on multiple data inputs.
How to create a cybersecurity analytics platform with AWS analytics and machine learning
Cybersecurity analytics is a systematic methodology designed to collect, ingest, process, aggregate, and analyze security events. This methodology empowers organizations to proactively perform security investigations, powered by advanced analytics and machine learning (ML), which help mitigate cyber issues more effectively and efficiently at scale. Learn about the core components of a cybersecurity analytics framework and how organizations can use AWS to design a cybersecurity analytics platform with analytics and ML services.
Amazon SageMaker Studio Lab helps educators focus on teaching rather than technology
The browser-based computational notebook tool, Jupyter, provides students and educators with an interactive learning environment to accelerate programming learning. But setting up collaborative Jupyter notebooks at the classroom and institutional level can be time-consuming and costly. Amazon SageMaker Studio Lab is a no-cost service built on Jupyter notebooks that takes care of the configuration and security of setting up multi-user Jupyter notebook environments – so educators can focus on teaching and learners can accelerate their journey in ML.