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ZenML — how is it different from Kubeflow?

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Answer

  • ZenML: framework-agnostic, Python-first pipelines running on any backend (Airflow, Kubeflow, Vertex, Sagemaker, local).
  • Decorator-based (@step, @pipeline).
  • Aim: 'write pipeline once, deploy anywhere'.
  • Emphasizes reusability of components across projects.
  • Lightweight vs Kubeflow's monolith.
  • Younger project.
  • Best for teams that use multiple clouds / orchestrators.
Check yourself — multiple choice
  • Random
  • ZenML: framework-agnostic Python pipelines runs on any backend (Airflow/KFP/Vertex/SageMaker/local); decorator @step/@pipeline; 'write once deploy anywhere'; lightweight vs Kubeflow; multi-cloud teams
  • Same as Kubeflow
  • Not real

ZenML: framework-agnostic + decorator + write-once deploy-anywhere.

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