클라우드 네이티브 아키텍처 엔지니어링 플레이북 & 벤치마크 리포트 — Battle-tested engineering know-how from production environments

Engineering Playbook is a comprehensive cloud-native architecture engineering playbook and benchmark report collection. It covers Amazon EKS infrastructure optimization, Agentic AI platform engineering, AIOps/AIDLC methodologies, and performance benchmarks — all built from real production experience.

This knowledge base captures architecture patterns, performance optimization strategies, troubleshooting guides, and quantitative benchmark results from actual operations. Each technical domain includes both implementation guides and measurable performance data to support data-driven architecture decisions.

Live Documentation: https://devfloor9.github.io/engineering-playbook/

Cilium ENI, Gateway API migration, CoreDNS tuning, Karpenter autoscaling, East-West traffic optimization, and cost management — practical guides to maximize EKS infrastructure performance and efficiency.

GitOps-based cluster operations with Argo CD, node monitoring agent deployment, EKS debugging and resiliency guides — methodologies for reliably operating large-scale Kubernetes clusters.

End-to-end guides for building generative AI platforms on EKS — covering vLLM, llm-d, MoE model serving, NeMo Framework, Inference Gateway, Milvus vector DB, Kagent, Amazon Bedrock AgentCore with MCP, RAGAS evaluation, and Langfuse monitoring. From GPU resource management to agent observability across the full stack.

AI-powered operations and AI Development Lifecycle — OpenTelemetry observability, CloudWatch AI integration, DevOps Guru predictive operations, MLOps pipelines with Kubeflow and MLflow, SageMaker-EKS integration, and comprehensive AIDLC framework for production AI systems.

EKS Hybrid Nodes adoption, SR-IOV with DGX H200, file storage strategies, Harbor registry integration — bridging on-premises GPU infrastructure and cloud-native platforms.

Identity-First Security with EKS Pod Identity, GuardDuty Extended Threat Detection, Kyverno policy management, supply chain security — enterprise-grade security governance for modern platforms.

Red Hat OpenShift on AWS installation, security, and compliance guides for enterprise Kubernetes adoption.

Comprehensive performance benchmarks across infrastructure, AI/ML, and hybrid domains — including CNI comparisons (VPC CNI vs Cilium), Gateway API implementation benchmarks, AI/ML workload analysis, and security operations metrics. All reports follow reproducible methodology with statistical rigor.

• Container Orchestration: Amazon EKS, EKS Auto Mode, Karpenter
• Networking: Cilium, Gateway API, CoreDNS
• AI/ML Serving: vLLM, llm-d, NeMo Framework, Amazon Bedrock AgentCore
• MLOps: Kubeflow, MLflow, KServe, SageMaker
• Vector Databases: Milvus
• Observability: Prometheus, Grafana, Langfuse, Hubble, OpenTelemetry, CloudWatch AI
• GitOps: Argo CD
• Security: Kyverno, GuardDuty, EKS Pod Identity
• AI Agents: Kagent, MCP (Model Context Protocol)
• Evaluation: RAGAS

# Install dependencies
npm install

# Start dev server
npm start

# Production build
npm run build

Requires Node.js >=20.0 and Docusaurus 3.9.2

docs/
├── intro.md                          # Introduction
├── infrastructure-optimization/      # Infrastructure Optimization
├── operations-observability/         # Operations & Observability
├── agentic-ai-platform/             # Agentic AI Platform
├── aiops-aidlc/                     # AIops & AIDLC
├── hybrid-infrastructure/            # Hybrid Infrastructure
├── security-governance/              # Security & Governance
├── rosa/                             # ROSA
└── benchmarks/                       # Benchmark Reports

Issues, PRs, and feedback are all welcome. See GitHub Issues for details.

Content in this project is available under the MIT License.