Add Dagster orchestration and reduce active brands in backend

app/intelligence/artifacts/archive/README.md

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+# Archived model artifacts
+
+These three models still have **all of their training code, CLI flags and API
+options intact**. Only the pre-trained `.joblib` bundles were moved out of the
+loaded directory, because nothing in the application ever reads them back.
+
+| Artifact | Why archived |
+|---|---|
+| `demand_forecast_model.joblib` | `train_forecast` fits it and writes the `demand_forecast` table, but `store_db.get_latest_demand_forecast()` has **zero callers** - no endpoint, service or MCP tool consumes the forecast. |
+| `store_performance_model.joblib` | Referenced only from `ml_training_service.train_store_performance`. No inference consumer. |
+| `purchase_propensity_model.joblib` | Referenced only from `ml_training_service.train_purchase_propensity`. No inference consumer. |
+
+They are excluded from `train_all()`'s **default** set
+(`ml_training_service.PRODUCTION_MODELS`), not from the codebase. To rebuild one:
+
+```
+python scripts/train_ml_models.py --models store_performance
+```
+
+or `POST /api/admin/store-intelligence/train {"models": ["store_performance"]}`.
+
+Training writes to `MODEL_ARTIFACTS_DIR` (the parent directory), so a retrain
+promotes the model back to production automatically - wire up an endpoint that
+reads it first, or it will simply sit there unread again.