Scope MLflow experiment names to include user ID (Task 14.2)

- Updated FastAPI /training/start endpoint to extract X-User-ID header via get_user_id() dependency
- Modified _run_training_background to accept and use user_id parameter
- Added MLflow experiment setup with user scoping: experiments are named user_{user_id}_training when user_id is provided, falling back to default experiment name otherwise
- Updated database record insertion to store scoped experiment name
- Updated training/train.py train() function to accept user_id parameter and use it for experiment naming
- Mark task 14.2 as complete in tasks.md

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

services/ml/training/train.py

@@ -172,33 +172,40 @@ def compute_config_hash(config: PipelineConfig) -> str:
 def train(
     config: PipelineConfig,
     labeled_data_path: Path,
-    output_model_path: Optional[Path] = None
+    output_model_path: Optional[Path] = None,
+    user_id: Optional[str] = None
 ) -> str:
     """
     Main training function.
-    
+
     Loads labeled data, splits, trains model, evaluates, logs to MLflow,
     and stores metadata in PostgreSQL.
-    
+
     Args:
         config: Pipeline configuration
         labeled_data_path: Path to labeled CSV file
         output_model_path: Optional path to save model locally (for inference)
-        
+        user_id: Optional user ID for scoped experiment naming (e.g., user_{uuid}_training)
+
     Returns:
         MLflow run ID
     """
     training_config = config.stages.training
     mlflow_config = training_config.mlflow
-    
+
     # Initialize database
     init_db()
-    
+
     # Set MLflow tracking URI
     mlflow.set_tracking_uri(mlflow_config.tracking_uri)
-    
-    # Set experiment
-    mlflow.set_experiment(mlflow_config.experiment_name)
+
+    # Set experiment with user scoping if user_id is provided
+    if user_id:
+        experiment_name = f"user_{user_id}_training"
+    else:
+        experiment_name = mlflow_config.experiment_name
+
+    mlflow.set_experiment(experiment_name)
     
     logger.info(f"Loading labeled data from {labeled_data_path}")
     df = pd.read_csv(labeled_data_path)
@@ -247,7 +254,7 @@ def train(
             training_run = TrainingRun(
                 run_id=run_id,
                 model_type=training_config.model_type,
-                experiment_name=mlflow_config.experiment_name,
+                experiment_name=experiment_name,
                 pipeline_config_hash=compute_config_hash(config),
                 dataset_version=None,  # TODO: Add DVC hash if available
                 metrics_summary={},