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An enterprise machine learning team has defined an automated model retraining workflow using the Kubeflow Pipelines (KFP) SDK and compiled the pipeline specification into a YAML file stored in Cloud Storage.
The team wants to implement an event-driven architecture that triggers a new Vertex AI Pipeline execution whenever upstream data ingestion completes. The data ingestion service publishes a notification containing dynamic runtime parameter values.
Which architecture should you implement to trigger the Vertex AI Pipeline?
Configure an event-driven Cloud Function with a Cloud Pub/Sub trigger that decodes incoming message payloads, extracts runtime parameter values, and calls the Google Cloud Vertex AI SDK to instantiate and submit an aiplatform.PipelineJob.
Configure an Eventarc trigger directly on Cloud Storage to invoke Cloud Dataflow, which compiles the pipeline and executes the retraining steps directly.
Configure a Cloud Scheduler cron job with an HTTP target that directly invokes the Vertex AI Pipelines REST API endpoint on a fixed interval.
Configure Cloud Build to poll the Cloud Pub/Sub topic continuously, compile the Kubeflow pipeline YAML definition, and submit the pipeline job via the gcloud CLI.
Configure an event-driven Cloud Function with a Cloud Pub/Sub trigger that decodes incoming message payloads, extracts runtime parameter values, and calls the Google Cloud Vertex AI SDK to instantiate and submit an aiplatform.PipelineJob.
This pattern uses an event-driven Cloud Function subscribed to a Cloud Pub/Sub topic to automate pipeline execution. When upstream processes publish messages containing dynamic parameters, the function parses the message and uses the Vertex AI SDK (google-cloud-aiplatform) to launch a pipeline run.
StudyCerts
StudyCertsbase64 payload from the Pub/Sub event and extracts key-value parameter mappings.aiplatform.init() and executes aiplatform.PipelineJob.submit() pointing to the compiled YAML template in Cloud Storage.This approach aligns with Google Cloud best practices for reactive, serverless MLOps workflows by bridging asynchronous event sources to Vertex AI Pipelines without requiring continuously running virtual machines.
Configure an Eventarc trigger directly on Cloud Storage to invoke Cloud Dataflow, which compiles the pipeline and executes the retraining steps directly.
Configure a Cloud Scheduler cron job with an HTTP target that directly invokes the Vertex AI Pipelines REST API endpoint on a fixed interval.
Configure Cloud Build to poll the Cloud Pub/Sub topic continuously, compile the Kubeflow pipeline YAML definition, and submit the pipeline job via the gcloud CLI.