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Generative AI is reshaping how organizations approach productivity, customer experiences, and operational capabilities. Across industries, teams are experimenting with generative AI to unlock new ways of working. Many of these efforts produce compelling proofs of concept (POC) that demonstrate technical feasibility. The real challenge begins after those early wins. Although POCs frequently demonstrate technical feasibility, …

Navigating the generative AI journey: The Path-to-Value framework from AWS Read More »

Amazon SageMaker JumpStart provides pretrained models for a wide range of problem types to help you get started with AI workloads. SageMaker JumpStart offers access to solutions for top use cases that can be deployed to SageMaker AI Managed Inference endpoints or SageMaker HyperPod clusters. Through pre-set deployment options, customers can quickly move from model …

Use-case based deployments on SageMaker JumpStart Read More »

Deploying and scaling foundation models for generative AI inference presents challenges for organizations. Teams often struggle with complex infrastructure setup, unpredictable traffic patterns that lead to over-provisioning or performance bottlenecks, and the operational overhead of managing GPU resources efficiently. These pain points result in delayed time-to-market, suboptimal model performance, and inflated costs that can make …

Best practices to run inference on Amazon SageMaker HyperPod Read More »

This is guest post by David Lord, Taylor Lord, Shiva Prasad, Anup Banasavalli Hiriyanagowda, Nikhil Chandra from Guidesly. Guidesly is reshaping how outdoor recreation is booked, run, and experienced. Founded in 2019, it began as a way to connect anglers, hunters, divers, and outdoor recreation enthusiasts with trusted guides, dive shops, and charters. It has …

How Guidesly built AI-generated trip reports for outdoor guides on AWS Read More »

Skills in Chrome let you discover, save and remix AI workflows — and repeat them instantly. ​ 

Agentic AI is transforming how organizations use generative AI, moving beyond prompt-response interactions to autonomous systems that can plan, execute, and complete complex multi-step tasks. While early proof of concepts in Agentic AI spaces excite business stakeholders, scaling them to production requires addressing scalability, governance, and security challenges. Amazon Bedrock AgentCore is an Agentic AI …

Spring AI SDK for Amazon Bedrock AgentCore is now Generally Available Read More »

Google is bringing people together in Washington D.C. at our AI for the Economy Forum. ​ 

Building effective reward functions can help you customize Amazon Nova models to your specific needs, with AWS Lambda providing the scalable, cost-effective foundation. Lambda’s serverless architecture lets you focus on defining quality criteria while it handles the computational infrastructure. Amazon Nova offers multiple customization approaches, with Reinforcement fine-tuning (RFT) standing out for its ability to teach …

How to build effective reward functions with AWS Lambda for Amazon Nova model customization Read More »

Amazon Bedrock regularly releases new foundation model (FM) versions with better capabilities, accuracy, and safety. Understanding the model lifecycle is essential for effective planning and management of AI applications built on Amazon Bedrock. Before migrating your applications, you can test these models through the Amazon Bedrock console or API to evaluate their performance and compatibility. …

Understanding Amazon Bedrock model lifecycle Read More »

Now available through Amazon Bedrock AgentCore, use AWS Agent Registry to discover, share, and reuse agents, tools, and agent skills across your organization. As enterprises scale to hundreds or thousands of agents, platform teams face three critical challenges: visibility (knowing what agents exist across the organization), control (governing who can publish and what becomes discoverable …

The future of managing agents at scale: AWS Agent Registry now in preview Read More »