devops-ai-playbook

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devops-ai-playbook

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ZIP 다운로드

- - ci.yml
  - ci.yml.disabled
- - mcp-servers.png
- claude-setup.md
- part1-beginner-concepts.md
- part1-system-design.md
- part2-workflow.md
- - grafana-dashboard.yml
- argo-cd.yml
- kustomization.yml
- namespace.yml
- secrets.yml
- - .env.example
  - aiops_all_lambda_code.py
  - app.py
  - deploy.sh
  - README.md
  - requirements.txt
  - setup-iam.sh
- - .env.example
  - .gitignore
  - docker-compose.yml
  - health-check.sh
  - image-service.js
  - mock-product-service.js
  - package.json
  - simple-product-service.js
  - update-service.js
- - .terraform.lock.hcl
  - main.tf
  - outputs.tf
  - provider.tf
  - terraform.tfvars
  - variables.tf
- Issues.md
- README.md
.gitignore
CLAUDE.md
README.md

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// repository documentation

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> A full end-to-end DevOps project with AIOps integration — so you can connect the dots between how AI is helping automate DevOps tasks today.

---

## Welcome

Hey everyone!

Welcome to my DevOps + AI series where we build an end-to-end DevOps project with an AIOps integration.

A lot of you have been asking: *"when are you going to share a full DevOps project?"*

Well — here we are.

In this series we will:

- Build microservices locally
- Use Claude and AI tools to assist development
- Deploy everything step by step
- Migrate the system to the cloud on AWS EKS
- Set up a full CI/CD pipeline with GitHub Actions
- Implement GitOps workflows with ArgoCD
- Integrate AIOps capabilities with AWS Bedrock

By the end of this series, you won't just know tools — you'll understand how real DevOps systems are designed and deployed.

---

## Repository Structure

```
DevOps-Practice-Guide/
├── docs/
│   ├── part1-system-design.md     # System design foundations (Part 1)
│   ├── part2-workflow.md          # Full workflow with AIOps (Part 2)
│   └── claude-setup.md            # Claude Code + MCP server setup
├── projects/
│   ├── README.md                  # EKS deployment guide (Part 3)
│   ├── boutique-microservices/    # The application (7 services)
│   ├── Infrastructure/            # Terraform for AWS provisioning
│   └── aiops-assistant/           # Bedrock Agent — Kira (Part 4)
├── gitops/
│   ├── argo-cd.yml                # ArgoCD Application manifest
│   ├── kustomization.yml          # Kustomize entry point
│   └── k8s/                       # All Kubernetes manifests
└── .github/
    └── workflows/ci.yml           # GitHub Actions CI pipeline
```

---

## Series Structure

### Claude Setup — AI Assistant Configuration
[`docs/claude-setup.md`](docs/claude-setup.md)

Before jumping into the project, this step walks through how Claude Code is configured as the AI assistant throughout this series.

Three things are set up:

**CLAUDE.md** — a project instruction file at the repo root that Claude reads automatically at the start of every session. It puts Claude in safe execution mode: explain what you're about to do and why before taking any action. This is important when working with live AWS infrastructure where silent commands can have real consequences.

**MCP Servers** — background processes that extend Claude's built-in capabilities. Four servers are configured in `~/.claude/settings.json`:

| Server | What it unlocks |
|--------|----------------|
| `awslabs.eks-mcp-server` | Query EKS clusters, inspect pods, stream logs, apply manifests |
| `awslabs.terraform-mcp-server` | Run Terraform commands, search provider docs, run Checkov scans |
| `awslabs.aws-pricing-mcp-server` | Live AWS pricing lookups and cost analysis reports |
| `awslabs.core-mcp-server` | MCP orchestration layer (deprecated, kept for compatibility) |

**Skills** — domain-specific knowledge packs that improve how Claude reasons about certain topics. The `terraform-skill` is installed, giving Claude deeper context for Terraform module patterns, testing strategies, security scanning, and CI/CD workflows specific to infrastructure-as-code.

---

### Part 1 — System Design Foundations
[`docs/part1-system-design.md`](docs/part1-system-design.md)

We start with system design concepts specifically for cloud and DevOps. This is important whether you're a beginner, intermediate, or senior engineer — because companies don't choose tools randomly. They think about architecture patterns, deployment strategies, scalability, reliability, and cost tradeoffs.

We cover 12 core system design pillars used in modern DevOps architectures, and connect each one directly to something running in this project.

---

### Part 2 — Understanding the Workflow
[`docs/part2-workflow.md`](docs/part2-workflow.md)

Before writing any code or deployment configs, you need to understand how the entire system flows:

- What services we're building and how they communicate
- How the pipeline works
- How code moves from developer → CI → deployment → production → AIOps

This is where the full picture comes together — including how AI fits into the workflow.

---

### Part 3 — DevOps Project Implementation
[`projects/README.md`](projects/README.md)

Then we actually build the project. You'll see:

- Docker containers and Docker Compose
- Kubernetes deployments on EKS
- CI/CD pipelines with GitHub Actions
- GitOps automation with ArgoCD
- Infrastructure provisioning with Terraform
- Observability with Prometheus and Grafana

---

### Part 4 — AIOps Integration
[`projects/aiops-assistant/README.md`](projects/aiops-assistant/README.md)

Finally, we explore how AI helps with:

- Monitoring and anomaly detection
- Log analysis at scale
- Incident response automation
- DevOps troubleshooting

Because modern DevOps is no longer just automation — it's **automation + intelligence**.

---

## Bonus Challenge

You'll get access to this entire repository.

But there's a catch.

The repository includes **intentional issues and troubleshooting tasks**.

Why? Because AI has made things easier. But if you want to grow as an engineer, you must learn how to break systems, debug systems, and fix systems.

Once you implement the project:

1. Fork the repository
2. Deploy the system
3. Troubleshoot the issues
4. Share what you learned — and tag me so I know you're building along

---

## Tech Stack

| Layer | Technology |
|-------|-----------|
| Application | React, Node.js, PostgreSQL |
| Containers | Docker, Docker Compose |
| Orchestration | Kubernetes (AWS EKS) |
| Infrastructure | Terraform |
| CI/CD | GitHub Actions |
| GitOps | ArgoCD + Kustomize |
| Monitoring | Prometheus + Grafana |
| Log Forwarding | AWS Fluent Bit → CloudWatch |
| AIOps | AWS Bedrock Agent (Kira) |
| AI Assistant | Claude Code + MCP Servers |

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