feat: add routed model training pipeline

tests/test_training_dataset.py

@@ -6,6 +6,7 @@ from lmpm.training.dataset import (
     TrainingDataError,
     load_sqlite_training_dataset,
 )
+from lmpm.training.workflow import train_sqlite_database
 
 MATERIAL_SCHEMA = """
 CREATE TABLE material_records (
@@ -150,3 +151,20 @@ def test_database_without_material_table_raises_expected_error(tmp_path):
 
     with pytest.raises(TrainingDataError, match="material_records"):
         load_sqlite_training_dataset(database_path)
+
+
+def test_sqlite_workflow_trains_and_saves_every_target_model(tmp_path):
+    database_path = create_database(tmp_path)
+    artifact_path = tmp_path / "lmpm.joblib"
+
+    bundle = train_sqlite_database(database_path, artifact_path)
+
+    assert artifact_path.exists()
+    assert set(bundle.models) == {
+        "is_cut_through",
+        "carbonized_edge_width",
+        "etching_depth",
+        "is_fire_smolder",
+        "pattern_clarity_score",
+        "presentation_balance_score",
+    }