About This Architecture

Secure GCP GKE ML platform combining Global External Load Balancer, Cloud Armor WAF, Cloud IAP, and VPC Service Controls to isolate workloads across Frontend, Service, and Batch namespaces. External traffic flows through Cloud Armor and IAP to React Dashboard pods, which invoke the Python API Orchestrator via an internal load balancer protected by Private Service Connect. The orchestrator triggers ML Kubernetes jobs that read and write to Cloud Firestore and Cloud Storage within the VPC Service Control perimeter. This architecture enforces least-privilege access, prevents data exfiltration, and scales GPU-backed batch jobs independently. Fork and customize this diagram to match your ML platform's security posture, namespace topology, and persistence layer choices.

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How do I architect a secure GCP GKE ML platform with VPC Service Controls and multi-namespace isolation?

This diagram shows a three-tier GKE ML platform where external traffic passes through Cloud Armor WAF and Cloud IAP before reaching React Dashboard pods in the Frontend namespace. The Python API Orchestrator in the Service namespace orchestrates ML jobs in the Batch namespace via an internal load balancer, with all data access to Cloud Firestore and Cloud Storage protected by a VPC Service Control