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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 …

Generate structured output from LLMs with Dottxt Outlines in AWS Read More »

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 »

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 »

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 …

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

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 »

This blog post was co-authored with Johannes Maunz, Tobias Bösch Borgards, Aleksander Cisłak, and Bartłomiej Gralewicz from Hexagon. Hexagon is the global leader in measurement technologies and provides the confidence that vital industries rely on to build, navigate, and innovate. From microns to Mars, Hexagon’s solutions drive productivity, quality, safety, and sustainability across aerospace, agriculture, …

Accelerating AI model production at Hexagon with Amazon SageMaker HyperPod Read More »

This post is cowritten by Jeff Boudier, Simon Pagezy, and Florent Gbelidji from Hugging Face. Agentic AI systems represent an evolution from conversational AI to autonomous agents capable of complex reasoning, tool usage, and code execution. Enterprise applications benefit from strategic deployment approaches tailored to specific needs. These needs include managed endpoints, which deliver auto-scaling capabilities, foundation …

Agentic AI with multi-model framework using Hugging Face smolagents on AWS Read More »

In 2025, Amazon SageMaker AI saw dramatic improvements to core infrastructure offerings along four dimensions: capacity, price performance, observability, and usability. In this series of posts, we discuss these various improvements and their benefits. In Part 1, we discuss capacity improvements with the launch of Flexible Training Plans. We also describe improvements to price performance …

Amazon SageMaker AI in 2025, a year in review part 1: Flexible Training Plans and improvements to price performance for inference workloads Read More »

In 2025, Amazon SageMaker AI made several improvements designed to help you train, tune, and host generative AI workloads. In Part 1 of this series, we discussed Flexible Training Plans and price performance improvements made to inference components. In this post, we discuss enhancements made to observability, model customization, and model hosting. These improvements facilitate …

Amazon SageMaker AI in 2025, a year in review part 2: Improved observability and enhanced features for SageMaker AI model customization and hosting Read More »

Amazon Quick supports Model Context Protocol (MCP) integrations for action execution, data access, and AI agent integration. You can expose your application’s capabilities as MCP tools by hosting your own MCP server and configuring an MCP integration in Amazon Quick. Amazon Quick acts as an MCP client and connects to your MCP server endpoint to access …

Integrate external tools with Amazon Quick Agents using Model Context Protocol (MCP) Read More »