- Train/evaluate per-location ML models (gradient boosting, SHAP)
- Feature engineering from order history
- Build/operate prediction, training, cache, and metrics endpoints
- Maintain tiered cache fallbacks (hit stale static)
- Integrate DB, object storage, cache/queue, maps API
- Run services on Kubernetes with CI/CD
- Split monolith into training + inference services
- Improve observability