Efficiently serve dozens of fine-tuned models with vLLM on Amazon SageMaker AI and Amazon Bedrock
AWS demonstrates how to efficiently serve multiple fine-tuned models using vLLM on Amazon SageMaker and Amazon Bedrock. The implementation includes kernel-level optimizations for Mixture of Experts models.
Why this matters: This enables enterprises to deploy and manage multiple specialized AI models cost-effectively while maintaining performance.
Building intelligent event agents using Amazon Bedrock AgentCore and Amazon Bedrock Knowledge Bases
AWS shows how to build intelligent event agents using Amazon Bedrock AgentCore and Knowledge Bases. The system maintains attendee preferences and provides personalized experiences.
Why this matters: This provides a template for organizations to create scalable, personalized AI assistants without extensive custom infrastructure development.
Train CodeFu-7B with veRL and Ray on Amazon SageMaker Training jobs
AWS published a technical guide for training the CodeFu-7B model using specific reinforcement learning methods. The process utilizes distributed computing on SageMaker.
Why this matters: It provides a replicable framework for organizations to train specialized, large-scale AI models efficiently.
Generate structured output from LLMs with Dottxt Outlines in AWS
An AWS blog post details a method for generating structured outputs from large language models. The approach uses the Dottxt Outlines framework within SageMaker.
Why this matters: This enables more reliable integration of LLMs into applications that require consistent data formats.
Global cross-Region inference for latest Anthropic Claude Opus, Sonnet and Haiku models on Amazon Bedrock in Thailand, Malaysia, Singapore, Indonesia, and Taiwan
AWS has expanded global cross-region inference for Anthropic's Claude AI models to five Southeast Asian countries. The announcement includes technical implementation guidance and quota management best practices.
Why this matters: Enables enterprises in these regions to deploy Claude models with improved resilience and lower latency for AI applications.
Introducing Amazon Bedrock global cross-Region inference for Anthropic’s Claude models in the Middle East Regions (UAE and Bahrain)
AWS has launched global cross-region inference for Anthropic's Claude AI models in the UAE and Bahrain. The post details model capabilities, resilience benefits, and includes implementation code.
Why this matters: Allows Middle Eastern businesses to build generative AI applications with enhanced performance and reliability.
Scaling data annotation using vision-language models to power physical AI systems
Bedrock Robotics uses vision-language models to analyze construction footage and generate labeled datasets for autonomous equipment training. This collaboration with AWS aims to scale data annotation.
Why this matters: Automates labor-intensive data preparation for physical AI systems in industrial settings.
How Sonrai uses Amazon SageMaker AI to accelerate precision medicine trials
Sonrai implemented an MLOps framework using Amazon SageMaker AI for precision medicine trials. The system maintains traceability and reproducibility required in regulated healthcare environments.
Why this matters: Enables compliant AI deployment in regulated clinical trial settings.
Accelerating AI model production at Hexagon with Amazon SageMaker HyperPod
Hexagon scaled AI model production by pretraining segmentation models using Amazon SageMaker HyperPod infrastructure. This collaboration with AWS accelerated their model development pipeline.
Why this matters: Reduces infrastructure management overhead for enterprise AI model training.
Agentic AI with multi-model framework using Hugging Face smolagents on AWS
Hugging Face smolagents library integrates with AWS services to build agentic AI solutions. The demonstration includes a healthcare agent with multi-model deployment and clinical decision support capabilities.
Why this matters: Simplifies development of specialized AI agents for domain-specific applications.
Amazon SageMaker AI in 2025, a year in review part 1: Flexible Training Plans and improvements to price performance for inference workloads
Amazon SageMaker AI introduced Flexible Training Plans and improved price performance for inference workloads in 2025. These were part of broader infrastructure enhancements.
Why this matters: These improvements help organizations manage AI training costs and optimize deployment efficiency.
Amazon SageMaker AI in 2025, a year in review part 2: Improved observability and enhanced features for SageMaker AI model customization and hosting
Amazon SageMaker AI enhanced observability, model customization, and hosting capabilities in 2025. These updates followed earlier infrastructure improvements.
Why this matters: Better observability and customization tools enable more sophisticated AI deployment and monitoring.
Integrate external tools with Amazon Quick Agents using Model Context Protocol (MCP)
AWS provides a six-step checklist for building or validating MCP servers to integrate external tools with Amazon Quick Agents. This guide details implementation requirements for third-party partners.
Why this matters: Enables developers to extend Amazon Quick's capabilities by connecting specialized tools through standardized protocols.
Build AI workflows on Amazon EKS with Union.ai and Flyte
AWS detailed how to orchestrate AI workflows using Flyte on Amazon EKS, integrating with AWS services including S3 Vectors.
Why this matters: Provides enterprises with a scalable method to deploy and manage complex AI pipelines in cloud environments.
Amazon Quick now supports key pair authentication to Snowflake data source
Amazon Quick Sight now supports key pair authentication for connecting to Snowflake data sources.
Why this matters: Enhances security for business intelligence tools accessing sensitive data in cloud data warehouses.
Build unified intelligence with Amazon Bedrock AgentCore
Amazon Bedrock AgentCore enables building unified intelligence systems, demonstrated through the Customer Agent and Knowledge Engine implementation. The platform integrates multiple AI capabilities.
Why this matters: Organizations can develop more cohesive AI systems rather than isolated applications, potentially improving efficiency.
Evaluating AI agents: Real-world lessons from building agentic systems at Amazon
Amazon has developed an evaluation framework for agentic AI systems with standardized assessment procedures and systematic metrics. The framework addresses complexity in real-world applications.
Why this matters: Standardized evaluation methods could help organizations better assess and compare different AI agent implementations.