AI

Build AI agents for business intelligence with Amazon Bedrock AgentCore

OPLOG, a technology-driven fulfillment company powered by AI and robotics, processes millions of items monthly across Türkiye, the United Kingdom, and Germany for major brands and global marketplaces. Operating a customer-agnostic fulfillment model where multiple brands share warehouse infrastructure, workers, and autonomous robots, OPLOG faced a challenge common to many B2B organizations: fragmented business data […]

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Build AI-powered dashboard automation agents with NLP on Amazon Bedrock AgentCore

Business analysts often wait days for dashboard modifications when responding to changing business requirements. Traditional processes involve submitting modification requests to IT teams, who interpret requirements, navigate API documentation, understand table schemas, and deploy changes. While this approach maintains proper oversight and quality control, it can result in multi-day turnaround times when rapid dashboard updates

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Technology usually creates jobs for young, skilled workers. Will AI do the same?

At any given time, technology does two things to employment: It replaces traditional jobs, and it creates new lines of work. Machines replace farmers, but enable, say, aeronautical engineers to exist. So, if tech creates new jobs, who gets them? How well do they pay? How long do new jobs remain new, before they become

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Announcing OpenAI-compatible API support for Amazon SageMaker AI endpoints

Today, Amazon SageMaker AI introduces OpenAI-compatible API support for real-time inference endpoints. If you use the OpenAI SDK, LangChain, or Strands Agents, you can now invoke models on SageMaker AI by changing only your endpoint URL. You don’t need a custom client, a SigV4 wrapper, or code rewrites. Overview With this launch, SageMaker AI endpoints

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Multimodal evaluators: MLLM-as-a-judge for image-to-text tasks in Strands Evals

If you’re building visual shopping, image or document understanding, or chart analysis, you need a way to verify whether your model’s response is actually grounded in the source image. A text-only evaluator cannot tell you whether a caption faithfully describes an image, whether an extracted invoice total matches the document, or whether a screen summary

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