Machine Learning on Kubernetes Birds of a Feather
summary
Roadmap and demos focus on making Kubeflow production ready with a stable API toward 1.0, plus a newly launched Kubeflow Pipelines workflow orchestrator that wires Python functions into multi-step, conditional pipelines; concrete components include TFJob operator, ModelDB for model tracking, Katib integration for hyperparameter tuning and neural architecture search, notebook CRDs to spawn reproducible notebook-backed jobs, Istio-based rollout/inference support, CSI-backed notebook/data snapshots, and examples runnable on minikube or cloud one‑click clusters.
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