Phase 6: Cloud Deployment in Client Environments · 55 min · Python · Kubernetes · GKE

Private Kubernetes & Workload Identity

No public API endpoint. No static credentials. No exceptions.

What you will learn

Deploy private GKE clusters: private control plane, private nodes, authorized networks
Configure Workload Identity: binding K8s service accounts to cloud IAM, eliminating static credentials
Deploy EKS private clusters: private API server, VPC endpoints
Configure node pools: GPU nodes for inference, CPU nodes for orchestration, autoscaling
Implement network policies: restricting pod-to-pod communication, egress controls, namespace isolation

Objective

What you'll learn

This lesson takes approximately 55 min. By the end, you will be able to:

Deploy private GKE clusters: private control plane, private nodes, authorized networks
Configure Workload Identity: binding K8s service accounts to cloud IAM, eliminating static credentials
Deploy EKS private clusters: private API server, VPC endpoints
Configure node pools: GPU nodes for inference, CPU nodes for orchestration, autoscaling
Implement network policies: restricting pod-to-pod communication, egress controls, namespace isolation

The Problem

Private Kubernetes clusters — with no public API endpoint — are the standard for enterprise AI deployments. You access them through a bastion host, VPN, or private peering. Workload identity replaces static credentials with short-lived tokens from the cloud IAM. This lesson covers private cluster architecture and workload identity configuration.

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Concept, Build, Key Terms & Next — plus a hands-on lab, quiz, and project artifact.

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