Kubernetes and MLOps for Scalable and Reproducible Generative AI
summary
Kubernetes plus MLOps is shown as a practical stack for scalable, reproducible generative AI: containers on Kubernetes, Kubeflow pipelines, Jupyter notebooks, TensorFlow/PyTorch training, and serving via Seldon Core or NVIDIA Triton. Claim: over 50% of models never reach production; at Shell Kubeflow enables self‑service onboarding that drops setup time from months to minutes and converts notebooks into automated pipelines with monitoring that releases cloud GPUs. Caveats: GPU scarcity, cost, and added infrastructure complexity remain.
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