Question

Your ML platform team runs a Vertex AI Pipeline that trains a model, evaluates it, and pushes it to the Vertex AI Model Registry. Compliance requires that no model version reach the production endpoint until a designated reviewer manually inspects the evaluation metrics and explicitly approves the release. You want to keep the training-through-registration steps fully automated while inserting this human approval before deployment, without rebuilding or retraining the model after approval. What is the best way to design this?

Accepted Answer

Split the workflow: an automated training/evaluation/registration pipeline that stops after registering the candidate version, and a separate deployment pipeline triggered only after a reviewer approves (e.g., via a Model Registry version alias/label update that fires an Eventarc/Pub-Sub trigger).

Answer

Add a KFP dsl.Condition step in the single pipeline that pauses execution and waits for the reviewer to type approval into the pipeline run's console before continuing to deployment.

Answer

Use an exit handler at the end of the pipeline that deploys the model automatically and emails the reviewer so they can roll back if the metrics were unacceptable.

Answer

Have the pipeline deploy the model to the production endpoint immediately, but set the traffic split to 0% until the reviewer manually raises it to 100%.