Kubeflow, TensorFlow on Kubernetes
summary
A practical overview of Kubeflow on Kubernetes showing how to manage a complete machine learning model lifecycle: editing with Jupyter, building training and prediction containers, running A/B tests, using TensorBoard, and changing model parameters. The session uses TensorFlow as the ML framework and demonstrates how Kubeflow helps deploy and operate models on Kubernetes.
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