Advanced GCP: GKE, BigQuery, and Vertex AI

Advanced GCP: GKE, BigQuery, and Vertex AI

Google Kubernetes Engine provides managed Kubernetes, BigQuery provides serverless analytical warehousing, and Vertex AI provides managed model, training, deployment, evaluation, and generative AI capabilities. Artifact Registry stores versioned container and language packages used by delivery pipelines.

Core ideas

• GKE Autopilot manages more node infrastructure, while Standard exposes more cluster and node control.
• BigQuery separates serverless query execution from managed columnar storage and supports partitioning and clustering.
• Vertex AI supports data, training, model registry, endpoints, pipelines, monitoring, and generative AI workflows.
• Workload Identity Federation for GKE maps Kubernetes workloads to Google Cloud identities without key files.

Design and operating model

Select the simplest platform matching workload and team needs. For GKE, define cluster ownership, release channel, identity, networking, policy, observability, and upgrades. For BigQuery, design partitions and query controls. For AI, govern data, evaluation, safety, deployment, monitoring, and rollback.

Example

apiVersion: apps/v1
kind: Deployment
metadata:
  name: learning-api
spec:
  replicas: 3
  selector:
    matchLabels: { app: learning-api }
  template:
    metadata:
      labels: { app: learning-api }
    spec:
      serviceAccountName: learning-api
      containers:
        - name: api
          image: asia-south1-docker.pkg.dev/PROJECT/apps/api@sha256:REPLACE
          resources:
            requests: { cpu: 100m, memory: 128Mi }
            limits: { memory: 256Mi }

Run examples in a disposable training project. Replace project IDs, Regions, identities, resource names, sizes, and policies with reviewed values, and verify current Google Cloud CLI and service requirements before production use.

Production guidance

• Use immutable digests, artifact scanning, provenance, workload identity, and policy-controlled delivery.
• Partition BigQuery tables and set query or reservation controls to prevent accidental cost spikes.
• Evaluate AI for quality, safety, bias, privacy, latency, cost, drift, and responsible use throughout its lifecycle.

The chapter quiz follows the lesson and checks both service vocabulary and architecture decisions.