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A CNCF article argues that operating enterprise AI infrastructure is not about deploying a single model or cluster, but about managing a shared fleet of GPU units for multiple teams while balancing utilization, isolation, and cost. The article presents technical layers covering provisioning, allocation, scheduling, networking, storage, monitoring, and billing.
The CNCF announced Kubeflow’s graduation, deeming it a mature platform ready for production use in managing the artificial intelligence and machine learning lifecycle on Kubernetes. The decision follows the project’s expansion to more than 6,600 contributors and nearly 260 million downloads of its Python packages from PyPI.