Aj Geddes Useful AI Prompts Deployment Automation

Deployment Automation

Table of Contents

• Overview
• When to Use
• Quick Start
• Reference Guides
• Best Practices

Overview

Establish automated deployment pipelines that safely and reliably move applications across development, staging, and production environments with minimal manual intervention and risk.

When to Use

• Continuous deployment to Kubernetes
• Infrastructure as Code deployment
• Multi-environment promotion
• Blue-green deployment strategies
• Canary release management
• Infrastructure provisioning
• Automated rollback procedures

Quick Start

Minimal working example:

# helm/Chart.yaml
apiVersion: v2
name: myapp
description: My awesome application
type: application
version: 1.0.0

# helm/values.yaml
replicaCount: 3
image:
  repository: ghcr.io/myorg/myapp
  pullPolicy: IfNotPresent
  tag: "1.0.0"
service:
  type: ClusterIP
  port: 80
  targetPort: 3000
resources:
  requests:
    memory: "256Mi"
    cpu: "250m"
  limits:
    memory: "512Mi"
    cpu: "500m"
autoscaling:
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Best Practices

✅ DO

• Use Infrastructure as Code (Terraform, Helm)
• Implement GitOps workflows
• Use blue-green deployments
• Implement canary releases
• Automate rollback procedures
• Test deployments in staging first
• Use feature flags for gradual rollout
• Monitor deployment health
• Document deployment procedures
• Implement approval gates for production
• Version infrastructure code
• Use environment parity

❌ DON'T

• Deploy directly to production
• Skip testing in staging
• Use manual deployment scripts
• Deploy without rollback plan
• Ignore health checks
• Use hardcoded configuration
• Deploy during critical hours
• Skip pre-deployment validation
• Forget to backup before deploy
• Deploy from local machines

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