diff --git a/predict_pipeline/config.yaml b/predict_pipeline/config.yaml
index a2d2586..99ba5bf 100644
--- a/predict_pipeline/config.yaml
+++ b/predict_pipeline/config.yaml
@@ -7,8 +7,8 @@ model:
   path: "C:\\repo\\Fahrsimulator_MSY2526_AI\\files_for_testing\\xgb_model_3_groupK.joblib"
 
 scaler:
-  use_scaling: False
-  path = "C:"
+  use_scaling: True
+  path: "C:\\repo\\Fahrsimulator_MSY2526_AI\\predict_pipeline\\normalizer_min_max_global.pkl"
 
 mqtt:
   enabled: true
diff --git a/predict_pipeline/predict_sample.py b/predict_pipeline/predict_sample.py
index 37411ee..65b51db 100644
--- a/predict_pipeline/predict_sample.py
+++ b/predict_pipeline/predict_sample.py
@@ -11,7 +11,14 @@ sys.path.append(r"c:\\repo\\Fahrsimulator_MSY2526_AI\\tools")
 import db_helpers
 import joblib
 
-_MODEL_CACHE = {}
+def _load_serialized(path: Path):
+    suffix = path.suffix.lower()
+    if suffix == ".pkl":
+        with path.open("rb") as f:
+            return pickle.load(f)
+    if suffix == ".joblib":
+        return joblib.load(path)
+    raise ValueError(f"Unsupported file format: {suffix}. Use .pkl or .joblib.")
 
 def getLastEntryFromSQLite(path, table_name, key="_Id"):
     conn, cursor = db_helpers.connect_db(path)
@@ -43,22 +50,13 @@ def callModel(sample, model_path):
     model_path = model_path.resolve()
 
     suffix = model_path.suffix.lower()
-    cache_key = str(model_path)
-
-    if cache_key in _MODEL_CACHE:
-        model = _MODEL_CACHE[cache_key]
+    if suffix in {".pkl", ".joblib"}:
+        model = _load_serialized(model_path)
+    elif suffix == ".keras":
+        import tensorflow as tf
+        model = tf.keras.models.load_model(model_path)
     else:
-        if suffix == ".pkl":
-            with model_path.open("rb") as f:
-                model = pickle.load(f)
-        elif suffix == ".joblib":
-            model = joblib.load(model_path)
-        elif suffix == ".keras":
-            import tensorflow as tf
-            model = tf.keras.models.load_model(model_path)
-        else:
-            raise ValueError(f"Unsupported model format: {suffix}. Use .pkl, .joblib, or .keras.")
-        _MODEL_CACHE[cache_key] = model
+        raise ValueError(f"Unsupported model format: {suffix}. Use .pkl, .joblib, or .keras.")
 
     x = np.asarray(sample, dtype=np.float32)
     if x.ndim == 1:
@@ -162,14 +160,42 @@ def sample_to_numpy(sample, drop_cols=("_Id", "start_time")):
 
     return np.asarray(sample)
 
-def scale_sample(sample, use_scaling=False):
-    if use_scaling:
-        # load scaler
-        # normalize
+def scale_sample(sample, use_scaling=False, scaler_path=None):
+    if not use_scaling or scaler_path is None:
         return sample
+    scaler_path = Path(scaler_path)
+    if not scaler_path.is_absolute():
+        scaler_path = Path.cwd() / scaler_path
+    scaler_path = scaler_path.resolve()
+    normalizer = _load_serialized(scaler_path)
+
+    # normalizer format from model_training/tools/scaler.py:
+    # {"scalers": {...}, "method": "...", "scope": "..."}
+    scalers = normalizer.get("scalers", {}) if isinstance(normalizer, dict) else {}
+    scope = normalizer.get("scope", "global") if isinstance(normalizer, dict) else "global"
+    if scope == "global":
+        scaler = scalers.get("global")
     else:
+        scaler = scalers.get("global", next(iter(scalers.values()), None))
+
+    # Optional fallback if the stored object is already a raw scaler.
+    if scaler is None and hasattr(normalizer, "transform"):
+        scaler = normalizer
+    if scaler is None or not hasattr(scaler, "transform"):
         return sample
 
+    df = sample.to_frame().T if isinstance(sample, pd.Series) else sample.copy()
+    feature_names = getattr(scaler, "feature_names_in_", None)
+    if feature_names is None:
+        return sample
+
+    # Keep columns not in the normalizer unchanged.
+    cols_to_scale = [c for c in df.columns if c in set(feature_names)]
+    if cols_to_scale:
+        df.loc[:, cols_to_scale] = scaler.transform(df.loc[:, cols_to_scale])
+
+    return df.iloc[0] if isinstance(sample, pd.Series) else df
+
 def main():
     config_file_path = Path("predict_pipeline/config.yaml")
     with config_file_path.open("r", encoding="utf-8") as f:
@@ -178,7 +204,7 @@ def main():
     database_path = cfg["database"]["path"]
     table_name = cfg["database"]["table"]
     row_key = cfg["database"]["key"]
-    use_scaling = cfg.get("scaler", {}).get("use_scaling", cfg.get("scaler", {}).get("use_scaler", False))
+    
 
     sample = getLastEntryFromSQLite(database_path, table_name, row_key)
     valid, sample = replace_nan(sample, config_file_path=config_file_path)
@@ -190,8 +216,12 @@ def main():
         return
     
     model_path = cfg["model"]["path"]
+    scaler_path = cfg["scaler"]["path"]
+    use_scaling = cfg["scaler"]["use_scaling"]
+
+    sample = scale_sample(sample, use_scaling=use_scaling, scaler_path=scaler_path)
     sample_np = sample_to_numpy(sample)
-    sample_np = scale_sample(sample_np, use_scaling=use_scaling)
+
     prediction = callModel(model_path=model_path, sample=sample_np)
 
     message = buildMessage(valid, prediction, config_file_path, sample=sample)
@@ -202,4 +232,4 @@ if __name__ == "__main__":
     main()
 
 
-# https://www.youtube.com/watch?v=Q09tWwz6WoI
+