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Building intelligent event agents using Amazon Bedrock AgentCore and Amazon Bedrock Knowledge Bases

Building intelligent event agents using Amazon Bedrock AgentCore and Amazon Bedrock Knowledge Bases

Large conferences and events generate overwhelming amounts of information—from hundreds of sessions and workshops to speaker profiles, venue maps, and constantly updating schedules. While basic AI assistants can answer simple questions about event logistics, most fail to deliver the personalized guidance and contextual awareness that attendees need to navigate complex, multi-day conferences effectively. More importantly, …

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Build an intelligent photo search using Amazon Rekognition, Amazon Neptune, and Amazon Bedrock

Build an intelligent photo search using Amazon Rekognition, Amazon Neptune, and Amazon Bedrock

Managing large photo collections presents significant challenges for organizations and individuals. Traditional approaches rely on manual tagging, basic metadata, and folder-based organization, which can become impractical when dealing with thousands of images containing multiple people and complex relationships. Intelligent photo search systems address these challenges by combining computer vision, graph databases, and natural language processing …

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Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs

Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs

The rapid advancement of artificial intelligence (AI) has created unprecedented demand for specialized models capable of complex reasoning tasks, particularly in competitive programming where models must generate functional code through algorithmic reasoning rather than pattern memorization. Reinforcement learning (RL) enables models to learn through trial and error by receiving rewards based on actual code execution, …

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Generate structured output from LLMs with Dottxt Outlines in AWS

Generate structured output from LLMs with Dottxt Outlines in AWS

This post is cowritten with Remi Louf, CEO and technical founder of Dottxt. Structured output in AI applications refers to AI-generated responses conforming to formats that are predefined, validated, and often strictly entered. This can include the schema for the output, or ways specific fields in the output should be mapped. Structured outputs are essential …

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Global cross-Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan

Global cross-Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan

Organizations across in Thailand, Malaysia, Singapore, Indonesia, and Taiwan can now access Anthropic Claude Opus 4.6, Sonnet 4.6, and Claude Haiku 4.5 through Global cross-Region inference (CRIS) on Amazon Bedrock—delivering foundation models through a globally distributed inference architecture designed for scale. Global CRIS offers three key advantages: higher quotas, cost efficiency, and intelligent request routing …

Global cross-Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan Read More »

Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)

Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)

We’re excited to announce the availability of Anthropic’s Claude Opus 4.6, Claude Sonnet 4.6, Claude Opus 4.5, Claude Sonnet 4.5, and Claude Haiku 4.5 through Amazon Bedrock global cross-Region inference for customers operating in the Middle East. This launch supports organizations in the Middle East to access Anthropic’s latest Claude models on Amazon Bedrock while …

Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain) Read More »

Scaling data annotation using vision-language models to power physical AI systems

Scaling data annotation using vision-language models to power physical AI systems

Critical labor shortages are constraining growth across manufacturing, logistics, construction, and agriculture. The problem is particularly acute in construction: nearly 500,000 positions remain unfilled in the United States, with 40% of the current workforce approaching retirement within the decade. These workforce limitations result in delayed projects, escalating costs, and deferred development plans. To address these …

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How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials

How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials

In precision medicine, researchers developing diagnostic tests for early disease detection face a critical challenge: datasets containing thousands of potential biomarkers but only hundreds of patient samples. This curse of dimensionality can determine the success or failure of breakthrough discoveries. Modern bioinformatics use multiple omic modalities—genomics, lipidomics, proteomics, and metabolomics—to develop early disease detection tests. …

How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials Read More »

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