alvis
c7edd92e15
ML serving: - LinUCB contextual bandit (disjoint, d=5 features: hour_sin/cos, is_overdue, task_age, priority) - /score endpoint replaces stub random; /reward endpoint for online learning - Per-user model state persisted to disk as JSON (survives restarts) - venv at ml/serving/.venv; start with pnpm dev from ml/serving Recommender: - Todoist fetch now extracts features (is_overdue, task_age_days, priority) - RemotePolicy calls ml/serving with 3s timeout; falls back to RandomPolicy - Reward sent to /reward on feedback (done=+1, snooze=0, dismiss=-1) Web Push: - VAPID keys in config; push_subscriptions table in DB - POST/DELETE /api/push/subscribe; GET /api/push/vapid-public-key - Service worker (public/sw.js): push → showNotification, notificationclick → focus/open - "notify me" button on tip page; registers SW + subscribes on permission grant Event bus: - services/api/src/events/bus.ts: typed EventEmitter wrapper - Subjects: signals.tip.served, signals.tip.feedback, signals.task.synced - Same publish/subscribe API NATS JetStream will implement — swap is mechanical Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
ml/
Python. Owns models, features, training, online scoring.
| Dir | Role | Phase |
|---|---|---|
serving/ |
FastAPI online scorer (/score), called by recommender |
1 |
features/ |
feature definitions + store adapter (Feast later) | 1 |
pipelines/ |
batch feature + training DAGs (Prefect/Airflow) | 4 |
registry/ |
MLflow-backed model registry integration | 4 |
experiments/ |
A/B assignment + multi-armed bandit policies | 4 |
notebooks/ |
research; never imported by production code | — |
Principles
- Every model has a model card in
registry/describing inputs, offline metrics, fairness checks, and rollout history. - Online inference must be stateless and < 50ms p99.
- Training reads from the offline feature store; serving reads from the online feature store; definitions are shared (no train/serve skew).
- Shadow deploys before any policy change that affects real users.