Question

Your ML platform team maintains several Vertex AI Pipelines across different product teams. Multiple pipelines all need an identical data-validation step that your team authored as a Kubeflow (KFP) component. Currently each team copies the component's Python source into their own repository, causing drift and inconsistent behavior when the validation logic is updated. You want teams to consume a single, centrally-maintained, versioned copy of the component with minimal duplication. What should you do?

Accepted Answer

Compile the component to YAML, publish versioned artifacts to Artifact Registry (KFP template repository), and have each team load the component by version reference at build time.

Answer

Enable component caching in every pipeline so the validation step is reused across runs without teams needing to copy the source.

Answer

Package the validation logic as a custom training container image and have each pipeline invoke it through a CustomJob operator.

Answer

Store the component's Python file in a shared Cloud Storage bucket and have each team download and paste it into their DSL at authoring time.