About This Architecture

GCP Machine Learning Platform Architecture integrates Vertex AI for training and prediction with a multi-zone VPC spanning presentation, application, data, and management subnets across us-central1-a and us-central1-b. Users access the platform through Cloud CDN and Cloud Armor WAF, routing via Cloud Load Balancing to Cloud Run API and Training Services that orchestrate Vertex AI pipelines, Cloud TPU accelerators, and BigQuery ML datasets. The data layer combines Cloud SQL primary-replica pairs, Cloud Bigtable feature store, Cloud Memorystore caching, and Cloud Firestore for model metadata, with Cloud Dataflow ETL, Cloud Pub/Sub event bus, and Cloud Storage artifact management completing the production-grade ML ops stack. This architecture demonstrates enterprise ML platform design with built-in security, scalability, and observability through Cloud Monitoring, Logging, IAM, and KMS. Fork and customize this diagram on Diagrams.so to match your Vertex AI training and serving topology, multi-region failover, or custom preprocessing workflows.

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How do I architect a production machine learning platform on Google Cloud with Vertex AI, Cloud Run, and BigQuery?

This diagram shows a complete GCP ML platform with Vertex AI Training Pipeline and Prediction Endpoints, Cloud Run services for API and training orchestration, BigQuery for ML datasets, Cloud TPU accelerators, and a multi-zone VPC with Cloud CDN, Cloud Armor WAF, and Cloud Load Balancing for secure, scalable inference and training workflows.