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I found Kubeflow Pipelines to be worse than vanilla Argo. Not only is the documentation poor for the python DSL that gets compiled to the Argo workflow spec but some operations are downright impossible to express in the DSL. Vanilla Argo + RBAC works wonderfully especially with Argo Server since v3+.

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I agree. For anything sophisticated you have to use the Kubernetes Python client combined with the KFP DSL. The compiled Argo Workflow spec can become such a mess that you quickly run into a size limitations -- especially if you choose to deploy a Python function directly (which then uses Kaniko to build the containers in cluster and run them via Argo Workflows). At that point I would rather use Argo directly. And KFP is generally quite behind in Argo Workflow versions.

And it's not like KFP makes CI/CD (MLOps) any easier than Argo Workflows itself.

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