fix: call init_db() on ML service startup to create training_runs table

services/ml/app/main.py

@@ -24,7 +24,7 @@ import mlflow.xgboost
 from sqlalchemy import update as sa_update, desc
 
 from app.config import load_config, PipelineConfig, get_default_config
-from app.db import get_db, TrainingRun
+from app.db import get_db, TrainingRun, init_db
 from app.preprocessing import preprocess_candles, extract_feature_columns
 from app.patterns import (
     TALIB_PATTERNS,
@@ -311,6 +311,9 @@ async def startup_event():
     """
     logger.info("Starting inference service...")
 
+    # Ensure training_runs table exists
+    init_db()
+
     # Mark any stale "running" records as failed — they belong to a previous
     # process and will never complete.
     try: