Production-grade architecture docs for AI/ML and distributed systems.
Real systems, real trade-offs, real engineering decisions.
This is a collection of architecture documents for production AI/ML systems I've built. Each document covers the system context, component interactions, data flow, deployment strategy, and — most importantly — the trade-offs that were made and why.
If you're designing systems and want to see real-world examples with honest trade-off discussions, this is for you.
Real-time video processing at the edge with cloud sync. Camera frames → OpenCV/MediaPipe inference → fallback detection queue → cloud dashboard.
Retrieval-Augmented Generation pipeline with ChromaDB vector search and LLM orchestration. Documents in → chunks → embeddings → semantic search → LLM response.
End-to-end ML lifecycle automation: experiment tracking → model registry → containerized serving → monitoring → drift detection.
| Layer | Technologies |
|---|---|
| Real-time Processing | OpenCV, MediaPipe, ONNX Runtime, WebSocket, gRPC |
| RAG & LLMs | LangChain, ChromaDB, HuggingFace Transformers, OpenAI API, FastAPI |
| MLOps | MLflow, DVC, Kubeflow, GitHub Actions, Docker, Kubernetes |
| Infrastructure | AWS (ECR, EKS, SQS, DynamoDB), Docker, Terraform |
| Observability | Prometheus, Grafana, Loki, OpenTelemetry |
| API Layer | FastAPI, gRPC, WebSocket, REST |
- First Principles — Every pattern is justified by concrete requirements, not trends.
- Trade-off Transparency — No architecture is perfect. Each doc explicitly calls out what was sacrificed and why.
- Operational Readiness — Designed to be operated, monitored, and debugged in production.
- Separation of Concerns — Clear boundaries for independent scaling, testing, and evolution.
- Resilience by Default — Assumptions fail, networks degrade, models drift. Architecture absorbs failure gracefully.
Development → 1x effort
Testing → 2x effort
Deployment → 3x effort
Operations → 10x effort
Debugging → 20x effort
Architecture → Saves you all of the above
Bad architecture turns every deployment into a crisis, every scaling event into a rewrite, and every new hire into an archaeology expedition. Good architecture makes the system boring — and boring systems are reliable systems.
Architecture discussions welcome. Open an issue or PR if you see an improvement, an alternative trade-off, or a pattern worth documenting.