Blog

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 »

As artificial intelligence and machine learning (AI/ML) workflows grow in scale and complexity, it becomes harder for practitioners to organize and deploy their models. AI projects often struggle to move from pilot to production. AI projects often fail not because models are bad, but because infrastructure and processes are fragmented and brittle, and the original …

Build AI workflows on Amazon EKS with Union.ai and Flyte Read More »

Modern enterprises face significant challenges connecting business intelligence platforms to cloud data warehouses while maintaining automation. Password-based authentication introduces security vulnerabilities, operational friction, and compliance gaps—especially critical as Snowflake is deprecating username password. Amazon Quick Sight (a capability of Amazon Quick Suite) now supports key pair authentication for Snowflake integrations, using asymmetric cryptography where RSA …

Amazon Quick Suite now supports key pair authentication to Snowflake data source Read More »

CEO Sundar Pichai’s remarks at the opening ceremony of the AI Impact Summit 2026 ​ 

Building cohesive and unified customer intelligence across your organization starts with reducing the friction your sales representatives face when toggling between Salesforce, support tickets, and Amazon Redshift. A sales representative preparing for a customer meeting might spend hours clicking through several different dashboards—product recommendations, engagement metrics, revenue analytics, etc. – before developing a complete picture …

Build unified intelligence with Amazon Bedrock AgentCore Read More »