Phase 8 — Capstone Projects
Weeks 19-20 of 20 | Portfolio-worthy end-to-end systems
Overview
These three capstone projects demonstrate that you can build complete, production-quality CV systems, not just train models. Each project combines skills from all previous phases into a coherent deliverable you can present in interviews.
Projects
| # | Project | Key Skills Demonstrated |
|---|---|---|
| 01 | Real-Time Object Detection Pipeline | YOLOv8-style inference + FastAPI + Docker + monitoring |
| 02 | Face Recognition System | Face detection + ArcFace embedding + FAISS search |
| 03 | Medical Image Segmentation | U-Net + Dice loss + MLflow + ONNX export |
How to Present These in Interviews
The Portfolio Narrative
Don't just say "I trained a YOLO model." Say:
"I built a real-time detection system that processes synthetic camera feeds at 30 FPS, deployed it as a FastAPI service with dynamic batching, containerized it with Docker, and added latency/throughput monitoring. The end-to-end pipeline goes from raw frames to JSON detection events in under 80ms."
The Numbers Rule
Every capstone should have:
- Latency benchmark (ms at p50 and p95)
- Throughput (fps or requests/second)
- Accuracy metric (mAP, Dice, top-1 accuracy)
- Model size (MB) and parameter count
What Interviewers Look For
- End-to-end thinking: can you take a problem from data to deployment?
- Engineering discipline: clean code, proper abstractions, error handling
- Metric-driven mindset: do you know how good your system actually is?
- Production awareness: can it handle load? can you monitor it?