Tag: KubeFlow
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From Notebook to Production: Building End-to-End ML Pipelines with Kubeflow, KServe, and Fractional GPU Sharing
Transitioning a machine learning model from an experimental Jupyter Notebook to a highly available, auto-scaling production endpoint is rarely a linear path. In an enterprise environment, this migration introduces severe operational friction—not just in terms of rewriting code, but in handling infrastructure efficiency. With modern workloads demanding massive compute resources, assigning an entire enterprise-grade GPU…