Category: HAQM Machine Learning

Many government agencies, like the National Oceanic and Atmospheric Administration (NOAA), participate in the AWS Open Data Sponsorship Program. In this post, we discuss how to use NOAA datasets in the Registry of Open Data on AWS using HAQM Bedrock Knowledge Bases.

In this post, we walk through a step-by-step guide to building your own Retrieval Augmented Generation (RAG) solution on AWS, complete with open-source instructions and best practices. Learn how to harness the same technological foundations that power enterprise-grade proposal automation while maintaining security, compliance, and content quality.

Many organizations validate their generative AI–based workloads on their emotional reaction to a foundation model’s output instead of real testing criteria. In this post, we show you how you can move away from vibe testing and effectively evaluate your HAQM Bedrock workloads for production.

To support education leaders on their AI journey, we’ve developed a practical guide that maps real-world challenges to proven AI use cases. While not exhaustive, these examples serve as inspiration—highlighting what others have achieved and helping you explore how AI can address your institution’s priorities

When natural disasters and conflicts create survival situations, the rapid response of the world’s hundreds of humanitarian organizations saves lives. Those organizations can save even more lives when they acquire information in pace with fast-evolving situations on the ground. HAQM is investing $25,000 in AWS credits and providing hands-on engineering support to help the United Nations’ emergency reporting service, ReliefWeb, get up-to-date crisis information to everyone who needs it—and make sure that no situation is forgotten.

In this blog post, we explore the AI for Teaching & Learning Framework on AWS, and discuss how it is addressing the evolving needs of higher education in the digital age.

In today’s digital age, access to engaging educational content remains a critical challenge globally. Worldreader, a pioneering nonprofit organization, is tackling this challenge head-on. In this post, we explore how combining cloud computing capabilities with generative AI on AWS has enabled Worldreader to scale their impact while maintaining the quality and educational value of their content.

In today’s complex procurement landscape, creating comprehensive and accurate statements of work scopes can present a challenge for university procurement teams. To address this growing challenge, the Digital Transformation Hub (DxHub) at Cal Poly—powered by HAQM Web Services (AWS) and part of the AWS Cloud Innovation Centers (CIC) program—took action. The DxHub collaborated with the university’s Strategic Business Services department to create “Scope Builder,” a generative AI-powered application that aims to revolutionize how procurement specialists develop work scopes for university requests for proposals (RFPs) and contracts.

In this post, we will discuss how MSBMI and CDHI joined forces to address UTHealth Houston’s reliance on faxing. The MSBMI and CDHI team—supported by UTHealth Houston Information Technology and HAQM Web Services (AWS)—built, deployed, and migrated a new solution called iDFax.

Personalized therapy for diseases such as cancer utilizes an individual’s unique genomic profile to guide treatment decisions. However, the effect and clinical significance of most genetic variants are uncertain. Accurate classification of the clinical significance of novel genetic variants requires extensive curation of peer-reviewed biomedical literature. In recent years, generative AI has demonstrated promising results in information extraction and text summarization. In this post, we explore how various AWS-native solutions can be used to create a secure, retrieval-augmented, and cost-effective biomedical chatbot designed to facilitate biocuration.