Question

Your team maintains a Vertex AI Pipeline (KFP v2) that trains and deploys a model. For a new pipeline, you need to reuse a model artifact that was produced outside of any pipeline run — it currently exists only as a set of files in a Cloud Storage bucket and is not tracked in Vertex ML Metadata. You want the downstream evaluation and deployment components to consume it as a proper Model artifact with lineage, without writing a custom component that re-uploads the files. What is the most appropriate approach?

Accepted Answer

Use the dsl.importer node to import the Cloud Storage URI as a Model artifact, specifying the artifact type and metadata so downstream components receive a tracked artifact.

Answer

Hard-code the Cloud Storage path as a string pipeline parameter and pass it directly to the evaluation and deployment components.

Answer

Add a dsl.ParallelFor loop over the bucket files to reconstruct the artifact within the pipeline before passing it downstream.

Answer

Enable pipeline component caching so the artifact from a previous run is automatically reused in the new pipeline.