Plan, create, and configure production-ready Google Kubernetes Engine (GKE) clusters using the golden path Autopilot configuration. Covers Day-0 checklist, Autopilot vs Standard, networking (private clusters, VPC-native, Gateway API), security (Workload Identity, Secret Manager, RBAC hardening), observability, scaling, cost optimization, and AI/ML inference. WHEN: create GKE cluster, provision GKE environment, design GKE networking, secure GKE, optimize GKE cost, GKE autoscaling, GKE inference, GKE upgrade, GKE observability, GKE multi-tenancy, GKE batch, GKE HPC, GKE compute class.
Managed Kubernetes platform on Google Cloud. Defaults to Autopilot mode unless Standard is explicitly required.
sysctl) are required.hostPath mounts to the host OS filesystem.gke-cluster-creation.Private Autopilot Clusters:
--enable-private-nodes for private node IP addresses.--enable-private-endpoint to disable public IP access to the control plane.--enable-master-authorized-networks and --master-authorized-networks=CIDR_BLOCK:
gcloud container clusters create-auto CLUSTER_NAME --region=REGION \
--enable-private-nodes \
--enable-private-endpoint \
--enable-master-authorized-networks \
--master-authorized-networks=CIDR_BLOCK
Workload Identity (IAM Binding):
KSA) to bind to the Google Service Account (GSA):
metadata:
annotations:
iam.gke.io/gcp-service-account: GSA_NAME@PROJECT_ID.iam.gserviceaccount.com
Autopilot Resource Requests:
limits to allow Autopilot to set defaults matching requests.Cluster Credentials:
--region (for regional clusters) or --zone (for zonal clusters) when fetching credentials:
gcloud container clusters get-credentials CLUSTER_NAME --region=REGION --quiet
Core Concepts: Architecture, cluster modes (Autopilot vs Standard), networking, scaling, and security model.
CLI Usage & Tool Reference: Tool preference hierarchy (MCP vs gcloud vs kubectl), gcloud container commands, and user preference overrides.
Client Libraries: Official Kubernetes and Google Cloud Container client libraries in Python, Go, Node.js, and Java.
MCP Usage: Connecting to and using the 23 structured GKE MCP tools for cluster management, K8s resources, and diagnostics.
Infrastructure as Code: Terraform examples for google_container_cluster (Autopilot), Kubernetes provider resources, and YAML samples.
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