Category: Amazon SageMaker AI

At the University of Arizona (U of A) College of Medicine – Phoenix, a translational neuroscience lab of eight researchers is pursuing a mission to find and repurpose existing drugs as potential treatments for rare pediatric disorders. Working with Amazon Web Services (AWS), the team connected four AWS services into an integrated research pipeline, using generative artificial intelligence (AI) and specialized foundation models to turn raw sequencing data into insights that help identify promising drug candidates. In this post, learn how the lab is using generative AI to expand its research capacity with its current members to pursue scientific breakthroughs for a patient population that is often overlooked.

This post shows how to build that on Amazon Web Services (AWS) using Amazon Bedrock, deployed in AWS GovCloud (US). You’ll deploy a working multi-agent workload, see how a supervisor coordinates specialized agents through the Converse API in Amazon Bedrock, and learn which AWS GovCloud (US) details break patterns copied from commercial Regions.

This post introduces the Geospatial Power Pack, a Kiro power package that turns Kiro into a unified, AI-assisted geospatial workspace. Kiro is an agentic development environment created by Amazon Web Services (AWS). It helps developers and teams turn prompts into executable specs, validate code correctness to find bugs that unit tests miss, and build across large codebases with parallel agents that learn from every session.

Learn how Somapa Information Technology PCL (SomapaIT), an AWS Partner, chooses Amazon Web Services (AWS) Cloud to implement DAC systems because of its global footprint, security, high availability, and scalability.

In this post, Marshall Moutenot shares how dynamical.org is making weather data products, including AI weather forecasts, accessible on AWS.

Australian public sector agencies can now harness AI’s transformative potential while maintaining ethical, transparent, and compliant systems with new practical guidance from Amazon Web Services (AWS). AWS is excited to announce that the AWS Australian Public Sector User Guide: Building Responsible AI systems with Amazon Bedrock and Amazon SageMaker AI is now available through AWS […]

In this post, you will learn how AWS technologies support the entire autonomous system lifecycle from AI-powered design and virtual prototyping in pre-deployment, to edge computing through services like AWS IoT Core and AWS IoT Greengrass and related solution guidance like Tactical Edge Application Deployment on AWS and Cloud Edge Global Access (CEGA) during deployment, through real-time mission operations, and finally to post-mission evolution using secure over-the-air updates and predictive maintenance.

In this post, we walk through creating a Retrieval Augmented Generation (RAG)–powered chat assistant using Amazon SageMaker AI and Amazon Kendra to query donor data on AWS.

Generative AI can automate tasks like code generation, testing, and documentation, but public sector customers must prioritize solutions that meet unique regulatory and operational requirements. Addressing these challenges with a secure and compliant generative AI solution can transform software development, helping organizations deliver projects on time while reducing costs. This post outlines the considerations and solutions to evaluate when selecting an AI coding solution.

For academic institutions leveraging cloud services, it is important to proactively manage and optimize cloud costs. In this post, we explore 10 tips to help control cloud costs using Amazon Web Services (AWS).