Category: Amazon Bedrock

Real missions break perfect prototypes. Through Project MAVERICK (Mission Autonomy Versatile Rapid Innovation and Capabilities Kit), Amazon Web Services (AWS) confronts this reality head-on—bringing cloud capabilities directly into the field to test autonomous systems where it matters most.

This blog discusses how AWS has supported more than 600 customers with over $90 million of technology to innovate in health. Forty-four percent of these customers employed AWS AI services, seeding AI innovation across the global health landscape and proving that cloud-powered AI can improve health and wellness for all. This blog highlights nine of those organizations deploying AI to save lives today, culminating in AWS’s largest single social impact investment in health: a landmark technology collaboration with the Fleming Initiative to build the world’s first AI-powered platform for combating antimicrobial resistance.

In this blog, learn how five durable pillars—full-stack builders, parallel decision-making, context as a competitive moat, disciplined prioritization, and trust at AI speed—can stabilize your AI product development strategy amid rapid technological change. Drawn from the AWS Product Acceleration team’s work with AI-native product leaders, these principles help organizations cut through the noise and convert AI-driven speed into real customer value rather than chaos.

Learn how researchers in the University of São Paulo Research Center in Greenhouse Gas Innovation (RCGI) greenhouse gas (GHG) program saw an opportunity to develop a system that enabled close monitoring of the forest using data systems and data spaces in the cloud. They created Digital Amazon, a distributed data space network with open access that integrates CO2 and greenhouse gas emissions data collected by the university with other data sources to support critical and timely climate action and intervention in the Amazon Forest.

In this post, we walk you through how FRESH is translating cloud-enabled analytics into practical tools that support resilient, trusted food systems, starting with a deep dive into dynamic shelf-life modeling. Specifically, we detail how AWS services such as Amazon Simple Storage Service (Amazon S3), AWS Glue, and Amazon SageMaker AI are used to build and train predictive models.

Healthcare is entering a new era. Advances in AI, data analytics, and cloud computing are creating opportunities ranging from accelerating drug discovery and enabling precision medicine to helping clinicians detect disease earlier and spend more time with patients. As healthcare organizations embrace these technologies, they face an equally important responsibility: safeguarding some of the world’s […]

Learn how between Amazon Web Services (AWS) and HUMANBULB, the community organization behind the AWS Sacramento User Group — became a model that other cloud companies and community leaders can replicate. In this post, we share what we built, what we learned, and how other AWS teams and community leaders can apply the same approach in their own cities.

The methodology described in this post translates subjective personality requirements into testable behaviors, versioned prompts, and documented boundaries. It addresses several dimensions of Responsible AI at AWS, an eight-dimension framework that guides how we build and evaluate AI systems.

This post explains how to build exactly that using Strands Agents and Amazon Bedrock. The entire solution runs in about 200 lines of Python code, and you can have it working on your computer after completing the pre-requisite steps.

Learn how Amazon Web Services (AWS) operates inside a scoped compute environment with an AWS Identity and Access Management (IAM) execution role, network segmentation, and defense-in-depth security meeting FISMA, FedRAMP, and DoD CCSRG standards.