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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?
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.
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