24. Production Stack Project

: Build Production Inference Stack

Objective: Deploy a complete production inference stack with all operational components operational.

Prerequisites:

• Kubernetes cluster (minimum 3 nodes with GPU support)
• Docker registry access
• 50GB storage for model artifacts
• Domain name for TLS certificates

Phase 1: Infrastructure Foundation

1. Deploy nginx ingress controller with TLS
2. Configure Prometheus and Grafana stack
3. Set up PostgreSQL with backup schedule
4. Deploy Redis cache cluster

Phase 2: Inference Serving

1. Build Docker image for inference server
2. Deploy 3-replica model server with GPU allocation
3. Configure health checks and readiness probes
4. Set up Prometheus metrics endpoint

Phase 3: Operational Excellence

1. Create Grafana dashboard with latency, throughput, GPU metrics
2. Configure alerting rules for error rate and latency
3. Set up log aggregation

Phase 4: Reliability

1. Implement Argo Rollouts with canary deployment
2. Configure automated rollback triggers
3. Create backup and disaster recovery runbook

Phase 5: Security

1. Configure API key authentication
2. Enable mutual TLS between services
3. Scan container images for vulnerabilities
4. Apply network policies

Verification Checklist:

# production-verification.yaml
checks:
  - name: "Load balancer routes traffic"
    command: "curl -I https://inference.example.com/health"
    expected: "200 OK"
    
  - name: "Metrics endpoint exposes required metrics"
    command: "curl http://metrics.internal/metrics | grep inference_latency"
    expected: "inference_latency_seconds present"
    
  - name: "Grafana dashboard loads"
    command: "curl -u admin:pass http://grafana.internal/api/dashboards/uid/prod-inference"
    expected: "200 OK"
    
  - name: "Rollback triggers function"
    command: |
      # Generate load that exceeds error threshold
      # Verify automatic rollback within 5 minutes
    expected: "Rollout reverts to previous version"
    
  - name: "Canary deployment increments traffic"
    command: |
      kubectl argo rollouts get rollout inference-rollout -o json | jq '.status.canary.status'
    expected: "Traffic weight increases correctly"
    
  - name: "Disaster recovery backup verified"
    command: |
      aws s3 ls s3://inference-backups/models/latest/
    expected: "Model artifacts present"

Deliverables:

1. Deployment manifests for all infrastructure components
2. Grafana dashboard JSON export
3. Alerting rules configuration
4. Backup verification script with output
5. Disaster recovery runbook documentation
6. Cost analysis report with optimization recommendations

Success Criteria:

• All health checks pass within 30 seconds of deployment
• P95 inference latency remains under 2 seconds under 100 concurrent requests
• Automatic rollback executes within 5 minutes of sustained error threshold breach
• Backup restoration completes within 30-minute RTO target
• No critical vulnerabilities in container image scan results
• Cost per inference request documented and tracked in Prometheus Course completion summary: All 24 chapters have established foundational knowledge for production local AI deployment, covering model optimization, containerization, serving architectures, observability, reliability patterns, and operational hardening.