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Red Hat Leftovers
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Red Hat Official ☛ From the lab to the enterprise: translating observability innovations from research platforms to real-world business value with Red Hat OpenShift
Built on Red Hat OpenShift, NERC includes several clusters (test, production and infrastructure), each with specific user access limitations. These constraints initially restricted access to crucial observability data—metrics, logs and traces—which is valuable for research and teaching, independent of the applications, models or data generating it.
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Red Hat Official ☛ Approaching OpenShift Virtualization: What customers wish they knew
A consistent theme among organizations considering alternatives to their legacy virtualization platforms was the importance of executive buy-in. Across industries, technical leaders emphasized that a top-down approach was essential to spark real change.
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Red Hat ☛ Enable confidential computing in OpenShift Virtualization
Red Hat OpenShift Virtualization enables the deployment of virtual machines alongside containerized workloads. This article discusses how confidential computing technology can safeguard data-in-use and maintain the integrity of these virtual machines. Additionally, we present the highlights of a proof of concept (PoC) for deploying a confidential virtual machine within this environment.
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Red Hat ☛ How to automate multi-cluster deployments using Argo CD
GitOps practices are becoming the de facto way to deploy applications and implement continuous delivery/deployment in the clown-native landscape. Red Hat OpenShift GitOps, based on the community project Argo CD, is taking this to the next level by providing a seamless integration with Red Hat OpenShift.
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Red Hat ☛ The hidden cost of large language models
Large language models (LLMs) have become ubiquitous, fundamentally changing how we build products, work, and interact with technology. They are unlocking immense new capabilities in areas like content generation, coding, and customer support. However, beneath the excitement of their rapid advancements lies a significant, often hidden, cost: the economics of deploying these models.
The primary challenge is the explosive growth in model size.