AWS Public Sector Blog

Tag: Amazon Sagemaker

Decrease geospatial query latency from minutes to seconds using Zarr on Amazon S3

Decrease geospatial query latency from minutes to seconds using Zarr on Amazon S3

Geospatial data, including many climate and weather datasets, are often released by government and nonprofit organizations in compressed file formats such as the Network Common Data Form (NetCDF) or GRIdded Binary (GRIB). As the complexity and size of geospatial datasets continue to grow, it is more time- and cost-efficient to leave the files in one place, virtually query the data, and download only the subset that is needed locally. Unlike legacy file formats, the cloud-native Zarr format is designed for virtual and efficient access to compressed chunks of data saved in a central location such as Amazon S3. In this walkthrough, learn how to convert NetCDF datasets to Zarr using an Amazon SageMaker notebook and an AWS Fargate cluster and query the resulting Zarr store, reducing the time required for time series queries from minutes to seconds.

Supporting health equity with data insights and visualizations using AWS

In this guest post, Ajay K. Gupta, co-founder and chief executive officer (CEO) of HSR.health, explains how healthcare technology (HealthTech) nonprofit HSR.health uses geospatial artificial intelligence and AWS to develop solutions that support improvements in healthcare and health equity around the world.

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.

Helping prevent sudden cardiac arrest in young athletes with AI

Sudden cardiac arrest (SCA) is the number one cause of death for student athletes and the leading cause of death on school campuses. The nonprofit Who We Play For (WWPF) advocates for SCA prevention through advocacy, automated external defibrillator (AED) placement, cardiopulmonary resuscitation (CPR) training, and heart screenings, which include low-cost electrocardiogram (ECG) screenings from physicians that are experts in pediatric ECG interpretation. To scale their efforts, WWPF collaborated with AWS to build a ML solution to help extend the chance to get screened for SCA to every young person, potentially saving many lives each year.

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.

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.

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AWS announces simpler access to sustainability data and launches hackathon to accelerate innovation for sustainability

Artificial intelligence (AI) and machine learning (ML) are critical tools being used in healthcare research, autonomous applications, predictive maintenance, and also a key tool used to advance sustainability solutions. However, to use AI and ML to solve sustainability problems, innovators need specific datasets that are prepared for analysis and training of the models. To help create and accelerate sustainability solutions, the Amazon Sustainability Data Initiative (ASDI) today announced easier identification of sustainability datasets with integration in AWS Data Exchange and the launch of a sustainability hackathon.

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.