Red Hat Leftovers
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Red Hat ☛ Managing Sensitive Assets Within Image Mode for Red Bait Enterprise Linux
Aside from naming and versioning, managing sensitive assets, like credentials, is one of the more challenging aspects in technology. So, why is it so difficult? Well, to start off. What may be considered a sensitive asset to one individual or organization may not be the same as another. Also, given that there are so many different ways that sensitive assets can be managed, there is no universally accepted method available.
The challenges that encompass how sensitive assets are handled also apply to image mode, a new method that enables building and deploying Operating Systems using similar tools and approaches as any other traditional container. In this article, we will discuss the types of sensitive assets that apply to image mode for RHEL specifically and how to design appropriate workflows to incorporate secure practices within all phases, from build, deployment, to runtime.
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Red Hat ☛ Easily upgrade hosted OpenShift Virtualization clusters on hosted control planes
One of the greatest benefits to OpenShift hosted control planes (HCP) is that it makes Red Hat OpenShift operational management easier through reduced upgrade complexity and downtime. For example, in a traditional situation you’d be upgrading both the control plane and the workers. With HCP, you’re just upgrading the workers in most cases, thus reducing the risk and turnaround time normally associated with standalone installations. For a visual representation of the difference between a standalone setup and HCP, see Figure 1.
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Role of Open Source in AI [Ed: Red Hat shilling buzzwords, as usual]
In this Tech Barometer podcast, Red Hat’s Bev Gunn and Richard Harmon take listeners into the collaborative world of open source software and explain why it can lead to responsible AI.
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TechRadar ☛ Why enterprise AI needs connected clouds [Ed: Red Hat has been reduced to infantile buzzwords-dropping; this is Red Hat Official ☛ what the company is promoting this week]
As AI evolves alongside the expansion of cloud computing, it gains significantly enhanced capabilities for storage, processing and data management. Modern enterprises are capitalizing on this by strategically integrating resources from on-premises, edge and cloud environments. This vital integration enables the deployment of powerful and efficient AI tools across various settings.
Effective cloud integration also allows organizations to balance the crucial need for data security with the substantial computing power required to train and deploy sophisticated AI models. Achieving this balance is essential for optimizing resource utilization and improving operational efficiency in a cost-effective manner.