Category: Artificial Intelligence

In this post, you’ll learn why probabilistic AI validation falls short in regulated industries and how Automated Reasoning checks use formal verification to deliver mathematically proven results. You’ll also see how customers across six industries use this technology to produce formally verified, auditable AI outputs, and how to get started.

In this post, you will learn how speculative decoding works and why it helps reduce cost per generated token on AWS Trainium2.

This post is cowritten by Renata Salvador Grande, Gabriel Bueno and Paulo Laurentys at Rede Mater Dei de Saúde. The growing adoption of multi-agent AI systems is redefining critical operations in healthcare. In large hospital networks, where thousands of decisions directly impact cash flow, service delivery times, and the risk of claim denials, the ability […]

In this post, we introduce the Generative AI Path-to-Value (P2V) framework, a structured approach to help you move generative AI initiatives from concept to production and sustained value creation.

We’re excited to announce the launch of Amazon SageMaker JumpStart optimized deployments. SageMaker JumpStart improved deployments address the need for rich and straightforward deployment customization on SageMaker JumpStart by offering pre-defined deployment configurations, designed for specific use cases. Customers maintain the same level of visibility into the details of their proposed deployments, but now deployments are optimized for their specific use case and performance constraint.

This post explores how Amazon SageMaker HyperPod provides a comprehensive solution for inference workloads. We walk you through the platform’s key capabilities for dynamic scaling, simplified deployment, and intelligent resource management. By the end of this post, you’ll understand how to use the HyperPod automated infrastructure, cost optimization features, and performance enhancements to reduce your total cost of ownership by up to 40% while accelerating your generative AI deployments from concept to production.

In this post, we walk through how Guidesly built Jack AI on AWS using AWS Lambda, AWS Step Functions, Amazon Simple Storage Service (Amazon S3), Amazon Relational Database Service (Amazon RDS), Amazon SageMaker AI, and Amazon Bedrock to ingest trip media, enrich it with context, apply computer vision and generative AI, and publish marketing-ready content across multiple channels—securely, reliably, and at scale.

With the new Spring AI AgentCore SDK, you can build production-ready AI agents and run them on the highly scalable AgentCore Runtime. The Spring AI AgentCore SDK is an open source library that brings Amazon Bedrock AgentCore capabilities into Spring AI. In this post, we build an AI agent starting with a chat endpoint, then adding streaming responses, conversation memory, and tools for web browsing and code execution.

This post demonstrates how Lambda enables scalable, cost-effective reward functions for Amazon Nova customization. You’ll learn to choose between Reinforcement Learning via Verifiable Rewards (RLVR) for objectively verifiable tasks and Reinforcement Learning via AI Feedback (RLAIF) for subjective evaluation, design multi-dimensional reward systems that help you prevent reward hacking, optimize Lambda functions for training scale, and monitor reward distributions with Amazon CloudWatch. Working code examples and deployment guidance are included to help you start experimenting.

This post shows you how to manage FM transitions in Amazon Bedrock, so you can make sure your AI applications remain operational as models evolve. We discuss the three lifecycle states, how to plan migrations with the new extended access feature, and practical strategies to transition your applications to newer models without disruption.