Category: Amazon Machine Learning

Without proper documentation, unhoused individuals face overwhelming barriers to stability and opportunity. But new technologies and tools address these problems while helping the organizations who serve vulnerable populations. One innovative solution called Kiip, powered by Amazon Web Services (AWS), takes a unique approach to this problem by empowering individuals with access and control over their own personal, vital documents.

Healthcare organizations across the nation are working to address clinician burnout and are more constrained than ever. That’s why Netsmart, an industry leader in electronic health records for human services and post-acute care, and Amazon Web Services joined forces to advance artificial intelligence for community-based care providers, through the development of an AI Data Lab. By combining the scale and power of AWS with the Netsmart CareFabric® platform, Netsmart is driving intelligent automation, generative AI, predictive analytics, natural language processing and risk models to improve care delivery and outcomes for community-based care providers.

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 […]

This walkthrough explores how to leverage Amazon Forecast to derive valuable business insights in satellite communications use-cases. Operations teams can quickly see accurate satellite capacity forecasts on a per beam basis. The benefits include lower cost via provisioning just the right amount of bandwidth, and a more streamlined customer experience since users will be less impacted by weather or surge events.

Since wildfires can double in size and intensity every three to five minutes, early detection and reduced response times are essential. Cloud technologies, including artificial intelligence (AI) and machine learning (ML), can help with this. Learn a high-level architecture to create a solution with AWS that uses AI to identify and classify wildfire smoke imagery and then rapidly alert and inform first responders about the location and condition of a fire incident.

Artificial intelligence (AI) and machine learning (ML) technologies are transforming many industries. But although public sector organizations are realizing the benefits of these technologies, there are many remaining challenges, including biases and a lack of transparency, that limit the wider adoption to unlock the full potential of AI and ML. In this post, learn a high-level framework for how AWS can help you address these challenges and provide better outcomes for constituents.

The NYUMets team, led by Dr. Eric Oermann at NYU Langone Medical Center, is collaborating with AWS Open Data, NVIDIA, and Medical Open Network for Artificial Intelligence (MONAI), to develop an open science approach to support researchers to help as many patients with metastatic cancer as possible. With support from the AWS Open Data Sponsorship Program, the NYUMets: Brain dataset is now openly available at no cost to researchers around the world.

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.

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.

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.