Category: Storage

Manufacturing organizations can now reduce simulation cycle times from days to hours by integrating Teamcenter Simulation with AWS ParallelCluster — eliminating high performance compute (HPC) queuing bottlenecks while maintaining full data traceability. The demand for high-fidelity engineering simulations such as Computational Fluid Dynamics (CFD), Finite Element Analysis (FEA), and Multi-physics analyses continues to grow as […]

This post is a technical deep dive. It explains the Sandbox’s multi-tenant isolation model along with its inherited security and cost controls. It then examines two production-grade multi-agent AIOps systems our teams built on top of it, including the LangGraph (an open source multi-agent orchestration framework) state model, Retrieval-Augmented Generation (RAG) design, OpenSearch query patterns, parallel root cause analysis (RCA) with self-falsification, and the human-in-the-loop safeguards that help make agentic recovery safe in production. Code samples are illustrative and simplified for readability.

In this post, we build on that foundation by constructing specialized AI agents for each data modality along with a supervisor agent that orchestrates cross-modal analysis using Amazon Bedrock AgentCore and Strands Agents SDK. We also train predictive AI models with Amazon SageMaker AI to predict patient outcomes from multimodal features. To further explore the implementation details and get hands-on experience, refer to the accompanying code repository.

In this two-part blog series, we show how you can build agents that interact with multimodal HCLS data, making it easier for end users to query, explore, and ask questions of the data. We build on previous guidance for multimodal data analysis, which demonstrates how to store, query, and analyze clinical, genomic, and medical imaging data using purpose-built AWS services.

In a previous blog post, we described how AUMOVIO built an AI-powered engineering assistant on Amazon Bedrock that generates automotive-grade embedded code. That system accelerates development

In this post, we present a deployable reference architecture that addresses both challenges simultaneously. We show how Amazon Bedrock AgentCore orchestrates a single AI agent that reasons across unstructured documents in Amazon S3 and structured data in Snowflake.

In this blog, learn how Kite, a Gilead Company, modernized its bioinformatics infrastructure by transitioning from fragmented, project-specific workflows to a standardized, cloud-enabled approach using AWS HealthOmics. Discover how this collaboration with AWS Professional Services enabled over 100 researchers to access scalable, self-service computational workflows through their existing laboratory systems—without requiring CLI or infrastructure expertise.

Renewable energy operators face a growing challenge: Managing increasingly large, complex, high-value portfolios across solar, wind, and battery energy storage system (BESS) while meeting tighter compliance, grid, and market demands. Many operational workflows remain reactive, manual, and fragmented—limiting both performance and scalability. However, GreenBridge.AI is innovating renewable energy operations through AI agents for greater automated efficiency and scalability.

In this post, we show you how Amazon Nova Sonic, Amazon Bedrock AgentCore, and Strands Agents come together in VEMA (Voice-Enabled Manufacturing Assistant)—an AI-powered voice assistant that lets workers speak naturally to get instant answers, hands-free, in English or Spanish, without ever leaving their workstation.

This post describes how BBVA used pilots to identify reusable ML patterns, standardize operational workflows, and design extensible MLOps templates that accelerate ML delivery while maintaining governance and flexibility across teams and business domains.