Amazon Elastic Kubernetes Service

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Multi-account support for Amazon SageMaker HyperPod task governance

Amazon Elastic Kubernetes Service, Amazon SageMaker HyperPod, Artificial Intelligence, AWS Identity and Access Management (IAM), Best Practices, Intermediate (200) /

GPUs are a precious resource; they are both short in supply and much more costly than traditional CPUs. They are also highly adaptable to many different use cases. Organizations building or adopting generative AI use GPUs to run simulations, run inference (both for internal or external usage), build agentic workloads, and run data scientists’ experiments. […]

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Build scalable containerized RAG based generative AI applications in AWS using Amazon EKS with Amazon Bedrock

Amazon Bedrock, Amazon Elastic Kubernetes Service, Generative AI, Technical How-to /

Generative artificial intelligence (AI) applications are commonly built using a technique called Retrieval Augmented Generation (RAG) that provides foundation models (FMs) access to additional data they didn’t have during training. This data is used to enrich the generative AI prompt to deliver more context-specific and accurate responses without continuously retraining the FM, while also improving

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How Hexagon built an AI assistant using AWS generative AI services

AI/ML, Amazon Bedrock, Amazon Bedrock Guardrails, Amazon Bedrock Knowledge Bases, Amazon Elastic Kubernetes Service, Customer Solutions, Generative AI /

This post was co-written with Julio P. Roque Hexagon ALI. Recognizing the transformative benefits of generative AI for enterprises, we at Hexagon’s Asset Lifecycle Intelligence division sought to enhance how users interact with our Enterprise Asset Management (EAM) products. Understanding these advantages, we partnered with AWS to embark on a journey to develop HxGN Alix,

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Automate Amazon EKS troubleshooting using an Amazon Bedrock agentic workflow

Amazon Bedrock, Amazon Bedrock Agents, Amazon Elastic Kubernetes Service, Analytics /

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

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