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Vertex AI Training & Serving — Google Cloud Architecture Template
Scheduled model retraining on Vertex AI from GCS datasets and BigQuery features, with models served behind a Cloud Run inference API.
Why this architecture works
- Scheduled retraining keeps models fresh against data drift without manual intervention.
- Versioned datasets in GCS plus BigQuery feature sources make every training run reproducible.
- A managed serving endpoint autoscales predictions and supports gradual model rollout.
- A thin Cloud Run API in front of the model endpoint handles auth, validation, and response shaping.
- Serving metrics feed Cloud Monitoring so latency and prediction-drift alerts fire early.
What's inside (9 resources)
Training Datasets
gcp-cloud-storage
Feature Source
gcp-bigquery
Vertex Training
gcp-vertex-ai
Retrain Trigger
gcp-scheduler
Model Serving Endpoint
gcp-ai-platform
Inference API
gcp-cloud-run
Training SA
gcp-iam
Training Logs
gcp-logging
Cloud Monitoring
gcp-monitoring
From template to running infrastructure
- Open this template in the CloudForge visual designer (free account, no credit card).
- Customize resources, names, and connections on the drag-and-drop canvas — or ask Vani, the AI architect, to adapt it.
- Generate production-ready Terraform or Pulumi in one click.
- Review the plan diff and security scan, then deploy with human approval.