AWS Public Sector Blog

Category: Amazon SageMaker

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.

Nara Space uses AWS to improve satellite image quality up to three times with deep learning

Nara Space Technology is a South Korea-based startup that builds nano satellite constellations and provides satellite data services to let customers quickly identify and address issues like changing climate conditions and disaster recovery to improve life on Earth. Nara Space provides solutions for nano satellite and small spacecraft system design, integration, development, and testing; enables satellite data analytics based on deep learning; and improves the visual quality of standard satellite imagery with its Super Resolution core technology. To do this, Nara Space uses AWS for secure, flexible, scalable, and cost-efficient cloud solutions.

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.

How public sector agencies can identify improper payments with machine learning

To mitigate synthetic fraud, government agencies should consider complementing their rules-based improper payment detection systems with machine learning (ML) techniques. By using ML on a large number of disparate but related data sources, including social media, agencies can formulate a more comprehensive risk score for each individual or transaction to help investigators identify improper payments efficiently. In this blog post, we provide a foundational reference architecture for an ML-powered improper payment detection solution using AWS ML services.

How researchers at UC Davis support the swine industry with data analytics on AWS

A research team led by Dr. Beatriz Martinez Lopez at UC Davis supports pig farmers with a data analytics platform that aggregates and analyzes animal health data to diagnose animal viruses and diseases. But this platform was primarily designed for analysts and data scientists. To truly transform animal disease management, Martinez-Lopez wants to put this data analytics tool into the hands of farmers around the world. So the research team is using the scalable, cost-effective tools of the AWS Cloud, along with a research grant letter of support from AWS, to make this optimized platform a reality.

How AWS uses AI to power interactive artwork at new Smithsonian exhibit

This fall, artist Suchi Reddy and Amazon Web Services (AWS), in collaboration with the Smithsonian FUTURES Exhibition, debuted me+you in Washington, DC, which embodies the collective answers to the question, “What do you want your future to look like?” me+you is an interactive work of art powered by artificial intelligence (AI) and machine learning (ML) and is the centerpiece of the Smithsonian FUTURES exhibition.

Fighting fraud and improper payments in real-time at the scale of federal expenditures

Since 2003, the US federal government has made approximately $1.7 trillion in improper payments, with an estimated $206 billion made in FY 2020 alone. Improper payments are now anticipated to increase proportionally to new levels of federal spending. How can agencies fight improper payments at this scale? And what tools can agencies use to address fraud, erroneous data submission and other causes of this problem? Agencies can use AWS to solve the multi-sided issues of payment integrity.

Edunation scales up to 32 times activity by boosting infrastructure with AWS

Using AWS, Edunation seamlessly responded to increasing demand during the COVID-19 pandemic. Edunation collaborates with top educational institutions across the Middle East and North Africa (MENA) region and provides all-in-one learning and school management solutions. Today, the EdTech is on a mission to push learning management systems (LMS) beyond virtual classrooms.

How to manage Amazon SageMaker code with AWS CodeCommit

How to manage Amazon SageMaker code with AWS CodeCommit

To help protect investments on ML, government organizations can securely store ML source code. Storing Amazon SageMaker Studio code in an AWS CodeCommit repository enables you to keep them as standalone documents to reuse in the future. SageMaker Studio provides a single, web-based visual interface where you can perform all ML development steps required to prepare data and build, train, and deploy models. Read on to learn the steps to configure a git-based repository on CodeCommit to manage ML code developed with SageMaker.

Academic medical research

Using artificial intelligence and machine learning to advance medical research

Academic medical centers (AMCs) are under pressure to reduce costs, innovate at scale, and improve operational performance. To do this, they’re turning to the cloud. Two AWS Partner Network (APN) Public Sector Partners used the cloud to create solutions for AMCs that use large datasets to help advance medical research and analyze genomic data. Learn how these two partners are building solutions in the cloud to help AMCs further their mission.