Choosing the right approach for generative AI-powered structured data retrieval

Favorite Organizations want direct answers to their business questions without the complexity of writing SQL queries or navigating through business intelligence (BI) dashboards to extract data from structured data stores. Examples of structured data include tables, databases, and data warehouses that conform to a predefined schema. Large language model (LLM)-powered

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Shared by AWS Machine Learning July 1, 2025

Build AWS architecture diagrams using Amazon Q CLI and MCP

Favorite Creating professional AWS architecture diagrams is a fundamental task for solutions architects, developers, and technical teams. These diagrams serve as essential communication tools for stakeholders, documentation of compliance requirements, and blueprints for implementation teams. However, traditional diagramming approaches present several challenges: Time-consuming process – Creating detailed architecture diagrams manually

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Shared by AWS Machine Learning June 30, 2025

Context extraction from image files in Amazon Q Business using LLMs

Favorite To effectively convey complex information, organizations increasingly rely on visual documentation through diagrams, charts, and technical illustrations. Although text documents are well-integrated into modern knowledge management systems, rich information contained in diagrams, charts, technical schematics, and visual documentation often remains inaccessible to search and AI assistants. This creates significant

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Shared by AWS Machine Learning June 30, 2025

Tailor responsible AI with new safeguard tiers in Amazon Bedrock Guardrails

Favorite Amazon Bedrock Guardrails provides configurable safeguards to help build trusted generative AI applications at scale. It provides organizations with integrated safety and privacy safeguards that work across multiple foundation models (FMs), including models available in Amazon Bedrock, as well as models hosted outside Amazon Bedrock from other model providers

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Shared by AWS Machine Learning June 27, 2025

AWS costs estimation using Amazon Q CLI and AWS Cost Analysis MCP

Favorite Managing and optimizing AWS infrastructure costs is a critical challenge for organizations of all sizes. Traditional cost analysis approaches often involve the following: Complex spreadsheets – Creating and maintaining detailed cost models, which requires significant effort Multiple tools – Switching between the AWS Pricing Calculator, AWS Cost Explorer, and

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Shared by AWS Machine Learning June 27, 2025

Using Amazon SageMaker AI Random Cut Forest for NASA’s Blue Origin spacecraft sensor data

Favorite The successful deorbit, descent, and landing of spacecraft on the Moon requires precise control and monitoring of vehicle dynamics. Anomaly detection provides a unique utility for identifying important states that might represent vehicle behaviors of interest. By producing unique vehicle behavior points, critical spacecraft system states can be identified

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Shared by AWS Machine Learning June 26, 2025

Structured data response with Amazon Bedrock: Prompt Engineering and Tool Use

Favorite Generative AI is revolutionizing industries by streamlining operations and enabling innovation. While textual chat interactions with GenAI remain popular, real-world applications often depend on structured data for APIs, databases, data-driven workloads, and rich user interfaces. Structured data can also enhance conversational AI, enabling more reliable and actionable outputs. A

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Shared by AWS Machine Learning June 26, 2025

Amazon Bedrock Agents observability using Arize AI

Favorite This post is cowritten with John Gilhuly from Arize AI. With Amazon Bedrock Agents, you can build and configure autonomous agents in your application. An agent helps your end-users complete actions based on organization data and user input. Agents orchestrate interactions between foundation models (FMs), data sources, software applications,

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Shared by AWS Machine Learning June 25, 2025