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Add Zoom as a data accessor to your Amazon Q index

For many organizations, vast amounts of enterprise knowledge are scattered across diverse data sources and applications. Organizations across industries seek to use this cross-application enterprise data from within their preferred systems while adhering to their established security and governance standards. This post demonstrates how Zoom users can access their Amazon Q Business enterprise data directly …

Scaling Laws for Native Multimodal Models

Building general-purpose models that can effectively perceive the world through multimodal signals has been a long-standing goal. Current approaches involve integrating separately pre-trained components, such as connecting vision encoders to LLMs and continuing multimodal training. While such approaches exhibit remarkable sample efficiency, it remains an open question whether such late-fusion architectures are inherently superior. In …

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Automate Amazon EKS troubleshooting using an Amazon Bedrock agentic workflow

As organizations scale their Amazon Elastic Kubernetes Service (Amazon EKS) deployments, platform administrators face increasing challenges in efficiently managing multi-tenant clusters. Tasks such as investigating pod failures, addressing resource constraints, and resolving misconfiguration can consume significant time and effort. Instead of spending valuable engineering hours manually parsing logs, tracking metrics, and implementing fixes, teams should …

Predicting material failure: Machine learning spots early abnormal grain growth signs for safer designs

A team of Lehigh University researchers has successfully predicted abnormal grain growth in simulated polycrystalline materials for the first time—a development that could lead to the creation of stronger, more reliable materials for high-stress environments, such as combustion engines. A paper describing their novel machine learning method was recently published in Nature Computational Materials.