Rolling Out AI in a Company: How to Fit It Into an Existing Network
dBrain.cloud built a two-tier AI infrastructure: LocalAI for quickly launching ready-made models and Kubeflow for the full MLOps cycle. The hardest part was not deploying the models but choosing a KServe mode. The team dropped Knative in favor of Standard mode, then moved its entire network architecture from Ingress to Gateway API to simplify operations, reduce the number of dependencies and remove unnecessary points of failure.
AI-processed from Habr AI; edited by Hamidun News
Rolling out AI in a company starts not with models but with how to fit them into the existing infrastructure. To do this, the dBrain platform put together two layers: LocalAI for ready-made models and Kubeflow for the full development cycle.
Where does the main difficulty of adopting AI in a company lie?
Most of the work turns out to be not in the models themselves but in how to fit them into the existing infrastructure and network.
What is LocalAI?
An open source tool for running ready-made AI models: chat, image and video generation, speech recognition and synthesis, and multimodal scenarios.
Where is the main difficulty of adopting AI in a company?
Not in the models themselves, but in integrating them into the existing corporate infrastructure and network.
What is the hardest part of adopting AI in a company?
Most of the work turned out to be not in the models themselves but in how to fit them into the existing infrastructure and network.
Why is embedding AI models into a corporate system harder than the
models themselves? Most of the work turned out to be not in the models themselves but in how to fit them into the existing infrastructure and network.
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Key Questions Answered
Where does the main difficulty of adopting AI in a company lie?
Most of the work turns out to be not in the models themselves but in how to fit them into the existing infrastructure and network.
What is LocalAI?
An open source tool for running ready-made AI models: chat, image and video generation, speech recognition and synthesis, and multimodal scenarios.
Where is the main difficulty of adopting AI in a company?
Not in the models themselves, but in integrating them into the existing corporate infrastructure and network.
What is the hardest part of adopting AI in a company?
Most of the work turned out to be not in the models themselves but in how to fit them into the existing infrastructure and network.
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