Question

Your ML team has built a Vertex AI Pipeline (compiled to a KFP pipeline spec) that trains and evaluates a model. Data scientists frequently update the training component code in a GitHub repository. You need a CI/CD process that automatically recompiles the pipeline and triggers a new pipeline run whenever code is merged into the main branch, while keeping the pipeline definition versioned and auditable. Which approach best meets these requirements?

Accepted Answer

Configure a Cloud Build trigger connected to the GitHub repository that, on merges to main, compiles the pipeline, uploads the pipeline artifact to a Cloud Storage bucket, and submits a pipeline run via the Vertex AI SDK.

Answer

Schedule the pipeline to run every hour using Cloud Scheduler and Cloud Functions so that any recently merged code changes are eventually picked up.

Answer

Ask data scientists to manually recompile and re-run the pipeline from a Vertex AI Workbench notebook each time they merge changes to main.

Answer

Enable Vertex AI Pipelines component caching so that merged code changes are automatically detected and only changed components are re-executed on the next run.