Category: Amazon Machine Learning

While Criminal Justice Information Services (CJIS)-compliant workloads can run in standard US AWS Regions, some Justice and Public Safety (JPS) agencies may choose AWS GovCloud (US) for their sensitive workloads. These organizations can securely access cutting-edge artificial intelligence (AI) models from AWS Standard US Regions through FIPS-validated endpoints ensuring that all data is securely encrypted with FIPS 140-3 encryption as required by CJIS. This access can maintain CJIS compliance through secure cross-partition connectivity.

In this blog post, you will learn how Weill Cornell Medicine used generative AI to build a secure, searchable interface for its historical medical archives and unlock new research opportunities while preserving the integrity of these one-of-a-kind materials.

Now you can save time and instill confidence in documents more quickly by using Amazon Quick Automate, which creates a set of agents to complete complex business tasks with reasoning. Amazon Quick Automate is a powerful multi-agent automation capability within Amazon Quick Suite that streamlines complex enterprise processes spanning multiple departments, systems, and applications. Read this post to learn more.

This post explores how prompt engineering—the art of effectively communicating with AI—can help educators guide their students in building powerful educational tools using PartyRock.

In this blog post, we demonstrate how public sector organizations can overcome the challenges that emerge when integrating Amazon Redshift and other AWS relational databases with Amazon Bedrock. You’ll learn how to utilize enterprise data to generate AI-powered analytical insights by using natural language queries through Amazon Bedrock Agents and Amazon Bedrock Knowledge Bases. This custom solution enables seamless connectivity between your structured data sources and generative AI applications, eliminating the need for data duplication and significantly reducing development effort.

This blog post provides a comprehensive overview of the custom large language model (LLM) development lifecycle for public sector use, emphasizing scientific methodology and measurable outcomes.

Traditional cloud migration approaches followed the established 7 Rs: Rehost, Refactor or re-architect, Relocate, Repurchase, Replatform, Retain, and Retire. However, we now see the emergence of an 8th R, Reinvention, which is transforming government agencies into dynamic public value engines. Read this post to learn more.

Through the innovative use of Amazon Bedrock and other AWS services, Save the Children España created a transformative solution that addresses a critical social challenge. This web application not only simplifies access to social benefits for vulnerable families, but also empowers social workers with better tools to support their communities. Read this post to learn more.

PartyRock, an Amazon Bedrock playground from Amazon Web Services (AWS), makes creating artificial intelligence (AI) applications simple and engaging—even if you have never written a line of code. With just a few steps, you can build your own AI assistant to help with homework, share with friends, or spark new creative ideas. Read this post to learn more.

In this post, we explore how a graph-based student assessment system powered by AWS services can bring automation, accuracy, and adaptability to the standards-alignment process. With this modern approach, EdTech platforms can deliver scalable, standards-compliant, and personalized learning experiences across K12 learning environments.