harden delivery pipeline and production runtime

app/ml/training.py

@@ -2,9 +2,8 @@ from __future__ import annotations
 
 import logging
 from dataclasses import dataclass
-from typing import Sequence
 
-from app.ml.feature_store import FeatureVector, FeatureStore
+from app.ml.feature_store import FeatureStore
 
 logger = logging.getLogger(__name__)
 
@@ -34,4 +33,4 @@ class TrainingPipeline:
     def export(self, artifact_id: str) -> TrainedArtifact:
         if artifact_id not in self._artifacts:
             raise KeyError(f"Artifact '{artifact_id}' nicht gefunden.")
-        return self._artifacts[artifact_id]
+        return self._artifacts[artifact_id]