I’m fortunate that I get to speak with hundreds of customers every year. These enterprises span the…
انتشار: 2026/07/27 15:29 UTC
I’m fortunate that I get to speak with hundreds of customers every year. These enterprises span the technology adoption curve—some who thrive on the leading edge, and others who prefer being as risk-averse as possible. But the AI era—ever-churning and bursting with exuberance—has united nearly all of them at this moment. Experiment time is over. Enterprises are focused on moving beyond theoretical AI pilots to operationalizing it at scale, optimizing costs, and governing its actions.Beyond those corporate mandates, which are important, I think what’s refreshing about my conversationsvia Red Hat Blog ift.tt/yvUz4qX
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AI is entering the era of large-scale, distributed, agentic systems. Applications coordinate multiple models, tools, and services, process millions of requests, and demand enormous amounts of computer capacity. At the same time, infrastructure costs continue to climb, and access to AI hardware remains constrained by supply chains, vendor roadmaps, and rapidly evolving accelerator technologies.For many organizations, this creates a new kind of dependency. They've embraced open models and retained ownership of their data, yet their AI strategy still depends on a single hardware ecosystem.That's via Red Hat Blog ift.tt/3QGdiE0
Many believe that building models is the most challenging thing in AI, but managing the data that powers those models is equally difficult. We’ve all been there: a model's performance shifts or a data analysis yields inconsistent results, and you’re left wondering, "Wait, which version of the dataset did I use for this training run or report?"But, there’s good news. If you’re working in a Red Hat environment, our new AI quickstart helps address this exact problem. It combines the orchestration power of Red Hat OpenShift AI with the "Git-for-data" versioning capabilities of lakeFS.The Avia Red Hat Blog ift.tt/37TAZMI
Deploying Single Node OpenShift (SNO) using the Agent-Based Installer on KVM provides a fast, repeatable way to create a fully functional OpenShift cluster without dedicatedvia kifarunix.com ift.tt/aPoKtQC
وقتی میگوییم agent نباید در production استفاده شود . #agent #ai t.me/unixmenshttps://www.business-standar…
Large language model (LLM) API costs have a way of sneaking up on a business. What begins as a promising chatbot prototype often transforms into an invoice nightmare as users consume tokens at an unpredictable rate.A significant portion of these costs stems from repetitive queries. Users often ask the same questions. "What is the return policy?" or "Can I send this back?" phrased in slightly different ways. Another hidden cost can be from questions that are not relevant to the application, topic, or business, or that may even be abusive.Traditional caching fails in this situation because it revia Red Hat Blog ift.tt/35NChap