work on model training

docs/rain_prediction.md

@@ -40,6 +40,7 @@ pip install -r scripts/requirements.txt
 - `scripts/train_rain_model.py`: strict time-based split training and metrics report.
 - `scripts/predict_rain_model.py`: inference using saved model artifact; upserts into
   `predictions_rain_1h`.
+- `scripts/run_rain_ml_worker.py`: long-running worker for periodic training + prediction.
 
 ## Usage
 ### 1) Apply schema update (existing DBs)
@@ -90,6 +91,18 @@ export DATABASE_URL="postgres://postgres:postgres@localhost:5432/micrometeo?sslm
 bash scripts/run_p0_rain_workflow.sh
 ```
 
+### 6) Continuous training + prediction via Docker Compose
+The `rainml` service in `docker-compose.yml` now runs:
+- periodic retraining (default every 24 hours)
+- periodic prediction writes (default every 10 minutes)
+
+Artifacts are persisted to `./models` on the host.
+
+```sh
+docker compose up -d rainml
+docker compose logs -f rainml
+```
+
 ## Output
 - Audit report: `models/rain_data_audit.json`
 - Training report: `models/rain_model_report.json`