Artificial Intelligence
Advancing AI agent governance with Boomi and AWS: A unified approach to observability and compliance
In this post, we share how Boomi partnered with AWS to help enterprises accelerate and scale AI adoption with confidence using Agent Control Tower.
Use Amazon SageMaker Unified Studio to build complex AI workflows using Amazon Bedrock Flows
In this post, we demonstrate how you can use SageMaker Unified Studio to create complex AI workflows using Amazon Bedrock Flows.
Accelerating AI innovation: Scale MCP servers for enterprise workloads with Amazon Bedrock
In this post, we present a centralized Model Context Protocol (MCP) server implementation using Amazon Bedrock that provides shared access to tools and resources for enterprise AI workloads. The solution enables organizations to accelerate AI innovation by standardizing access to resources and tools through MCP, while maintaining security and governance through a centralized approach.
Choosing the right approach for generative AI-powered structured data retrieval
In this post, we explore five different patterns for implementing LLM-powered structured data query capabilities in AWS, including direct conversational interfaces, BI tool enhancements, and custom text-to-SQL solutions.
Revolutionizing drug data analysis using Amazon Bedrock multimodal RAG capabilities
In this post, we explore how Amazon Bedrock’s multimodal RAG capabilities revolutionize drug data analysis by efficiently processing complex medical documentation containing text, images, graphs, and tables.
Build and deploy AI inference workflows with new enhancements to the Amazon SageMaker Python SDK
In this post, we provide an overview of the user experience, detailing how to set up and deploy these workflows with multiple models using the SageMaker Python SDK. We walk through examples of building complex inference workflows, deploying them to SageMaker endpoints, and invoking them for real-time inference.
Context extraction from image files in Amazon Q Business using LLMs
In this post, we look at a step-by-step implementation for using the custom document enrichment (CDE) feature within an Amazon Q Business application to process standalone image files. We walk you through an AWS Lambda function configured within CDE to process various image file types, and showcase an example scenario of how this integration enhances Amazon Q Business’s ability to provide comprehensive insights.
Build AWS architecture diagrams using Amazon Q CLI and MCP
In this post, we explore how to use Amazon Q Developer CLI with the AWS Diagram MCP and the AWS Documentation MCP servers to create sophisticated architecture diagrams that follow AWS best practices. We discuss techniques for basic diagrams and real-world diagrams, with detailed examples and step-by-step instructions.
AWS costs estimation using Amazon Q CLI and AWS Pricing MCP Server
In this post, we explore how to use Amazon Q CLI with the AWS Pricing MCP Server to perform sophisticated cost analysis that follows AWS best practices. We discuss basic setup and advanced techniques, with detailed examples and step-by-step instructions.
Tailor responsible AI with new safeguard tiers in Amazon Bedrock Guardrails
In this post, we introduce the new safeguard tiers available in Amazon Bedrock Guardrails, explain their benefits and use cases, and provide guidance on how to implement and evaluate them in your AI applications.