AWS Public Sector Blog

Category: Amazon Machine Learning

Optimizing your nonprofit mission impact with AWS Glue and Amazon Redshift ML

Nonprofit organizations focus on a specific mission to impact their members, communities, and the world. In the nonprofit space, where resources are limited, it’s important to optimize the impact of your efforts. Learn how you can apply machine learning with Amazon Redshift ML on public datasets to support data-driven decisions optimizing your impact. This walkthrough focuses on the use case for how to use open data to support food security programming, but this solution can be applied to many other initiatives in the nonprofit space.

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.

Using machine learning to customize your nonprofit’s direct mailings

Many organizations perform direct mailings, designed to support fundraising or assist with other efforts to help further the organization’s mission. Direct mailing workflows can use everything from a Microsoft Word mail merge to utilizing a third-party mailing provider. By leveraging the power of the cloud, organizations can take advantage of capabilities that might otherwise be out of reach, like customized personalization at scale. In this walkthrough, learn how organizations can utilize machine learning (ML) personalization techniques with AWS to help drive better outcomes on their direct mailing efforts.

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.

4 ways AWS Partners are using AI/ML to drive public sector transformation

As investments and adoption of artificial intelligence (AI) and machine learning (ML) continue to rise, there is tremendous potential for improved citizen experiences in the public sector. As government, education, and nonprofit organizations seek solutions for their challenges, AWS Partners are at the forefront of helping to solve those problems using AI and ML. Discover four AWS Partners using AI/ML to better society and improve lives.

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.

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.

3 ways tax agencies can use AI on AWS

To gain operational efficiencies and reduce workload burdens on employees, some state finance and tax agencies are leveraging robotic process automation (RPA) on AWS. RPA is a software tool that integrates with almost any system or application and performs manual, repetitive, time-consuming tasks. Tax agencies can use AI and ML to support the sheer size and scale of data they manage and to access and analyze all types of data with ease, including voice, video, and streaming data. Find out three ways AI and ML are creating measurable outcomes for tax agencies.

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.