AI Platform Engineering Guide

Complete guide to building production-ready AI platforms with CI/CD, monitoring, and observability.

Contents

Part 1: Foundation

• Architecture Patterns
• Tech Stack Selection
• Security Best Practices

Part 2: Development

• CI/CD Pipelines for AI
• Testing Strategies
• Environment Management

Part 3: Operations

• Monitoring & Observability
• Incident Response
• Cost Optimization

Part 4: Team Enablement

• Developer Experience (DevEx)
• Documentation Standards
• Training Programs

Tech Stack Coverage

AI/ML:

• LangChain, LangGraph, LlamaIndex
• Claude, GPT-4, Gemini
• Vector databases (Qdrant, Pinecone, Weaviate)

Platform:

• Kubernetes, Docker, Terraform
• GitHub Actions, GitLab CI, Jenkins
• Prometheus, Grafana, OpenTelemetry

Cloud:

• AWS (ECS, Lambda, Bedrock)
• GCP (Cloud Run, Vertex AI)
• Azure (Container Apps, OpenAI Service)

Who This Is For

• Platform engineers building AI infrastructure
• DevOps teams adding AI capabilities
• Engineering leaders planning AI adoption
• Startups building AI products

Contributing

This is a living document. PRs welcome!

About EXIDIAN

This guide is maintained by EXIDIAN Technologies - we build AI applications and platform infrastructure for enterprises.

Services:

• Discovery Workshop (₹75K, 1 week)
• AI Agent Pilot (₹5L, 30 days)
• Production Implementation (₹15L, 60 days)

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License

CC BY 4.0 - Free to use with attribution