GenAI in enterprise IT has been overhyped. However, some trailblazers hope narrow models will make diagnosing Kubernetes issues easier.
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Enterprises often encounter friction using Kubernetes (K8s for short) at scale to orchestrate large swaths of containers, not to mention an escalating number of clusters. And due to its complexity, diagnosing issues within Kubernetes isn’t all that easy. At the same time, IT is looking at AI to automate aspects of configuring, managing, and debugging complex back-end technologies.
“Trying to solve IT problems with AI is nothing new,” says Itiel Schwartz, co-founder and CTO at Komodor. “It typically overpromises and underdelivers.” Yet, although he was skeptical at first, he now sees promise in utilizing finely-tuned generative AI models to reduce barriers and streamline Kubernetes operations.
Fine-tuned AI for root cause analysis
Accuracy in AI models hinges on their training data sets. And today’s popular large language models(LLMs), like OpenAI’s GPT, Meta’s Llama, Anthropic’s Claude, or …