AWS Public Sector Blog

Category: Amazon SageMaker

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Improve road safety by analyzing traffic patterns with no-code ML using Amazon SageMaker Canvas

To improve safety and convenience, transportation agencies amass a substantial volume of data. However, these organizations encounter challenges in data accuracy validation due to issues related to data quality and occasional missing information. With the incorporation of new artificial intelligence and machine learning capabilities from Amazon Web Services (AWS), they can take advantage of no-code solutions to identify and address data gaps.

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Estimating physical climate heat risk with NASA Global Daily Downscaled Projections on ASDI

Climate risk consists of transition risk and physical risk. Transition risk represents regulatory and market-based risks while physical climate risk covers climate-related earth processes and its effects on the built and natural environment. In this blog post, we highlight how to use Amazon Web Services (AWS) to enrich your asset portfolio with open climate data hosted in AWS.

Building hybrid satellite imagery processing pipelines in AWS

Building hybrid satellite imagery processing pipelines in AWS

In this blog post, learn how companies operating in AWS can design architectures that maximize flexibility so they can support both cloud and on-premises deployment use cases for their satellite imagery processing workloads with minimal modifications. 

How the Imaging Data Commons migrated 40 million medical images using AWS DataSync

How the Imaging Data Commons migrated 40 million medical images using AWS DataSync

Learn how the National Cancer Institute Imaging Data Commons (IDC) team migrated the Imaging Data Commons data to AWS using AWS DataSync. Plus, learn how to get started with IDC data, which is accessible at no cost through the AWS Open Data Sponsorship Program.

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34 new or updated datasets available on the Registry of Open Data on AWS

This quarter, AWS released 34 new or updated datasets on the Register of Open Data. What will you build with these datasets? Read through this blog post for inspiration.

AWS branded background with text overlay that says "BriBooks improves children's creative writing with generative AI, powered by AWS"

BriBooks improves children’s creative writing with generative AI, powered by AWS

Generative artificial intelligence (generative AI) has the potential to play several important roles in education, transforming the way we teach and learn. This blog post looks at how one EdTech startup, BriBooks, is leveraging generative AI to assist young children with creative writing.

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Build population health systems to enhance healthcare customer experiences on AWS

As the amount of health data increases, different healthcare, life sciences, population health, and public health organizations are working to modernize their data infrastructure, unify their data, and innovate faster with technologies like artificial intelligence and machine learning (AI/ML). In this blog post, we dive deep on architecture guidance that enables healthcare providers to improve patient care.

Generative AI in education: Building AI solutions using course lecture content

Generative AI in education: Building AI solutions using course lecture content

The education sector has gone through a transformative technological change in the last few years. First, the pandemic created a rise in e-learning solutions, as teachers and students adopted digital platforms for communicating, teaching and learning, and managing academic information. These solutions show that students all over the world can get quality education over the […]

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.