diff --git a/.env.example b/.env.example
index ece7541..36481e4 100644
--- a/.env.example
+++ b/.env.example
@@ -44,3 +44,5 @@ TIMEOUT_SAVE_MODEL="300"               # Save model to MLFlow (5 min for artifac
 TIMEOUT_CLEANUP_DIRECTORY="60"         # Cleanup temporary directory (1 min)
 TIMEOUT_DELETE_FILE="60"               # Delete file from MinIO (1 min)
 TIMEOUT_UPDATE_DATABASE="30"           # Database update operations (30 sec)
+
+EXTRA_PIP_REQUIREMENTS="git+https://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@github.com/Aignosi/sientia-mlops-library.git"
diff --git a/model_manager/sientia/model_serving.py b/model_manager/sientia/model_serving.py
index a31388c..41b50bb 100644
--- a/model_manager/sientia/model_serving.py
+++ b/model_manager/sientia/model_serving.py
@@ -104,14 +104,10 @@ class ModelServing:
             The GitHub token is hardcoded. Consider moving to environment variable
             or using a secure secret management solution (e.g., K8s secrets).
         """
-        # SECURITY: Token should be in environment variable, not hardcoded
-        # TODO: Replace with: os.getenv('GITHUB_TOKEN') or use K8s secrets
         mlflow.sklearn.log_model(
             sk_model,
             artifact_path,
-            extra_pip_requirements=[
-                'git+https://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@github.com/Aignosi/sientia-mlops-library.git'
-            ],
+            extra_pip_requirements=[os.getenv('EXTRA_PIP_REQUIREMENTS')],
             **kwargs,
         )
 
diff --git a/model_manager/sientia/models.py b/model_manager/sientia/models.py
index 267efca..4477297 100644
--- a/model_manager/sientia/models.py
+++ b/model_manager/sientia/models.py
@@ -8,6 +8,12 @@ from sklearn.base import BaseEstimator, TransformerMixin
 from sklearn.linear_model import LinearRegression
 from sklearn.preprocessing import StandardScaler
 
+DISCONTINUITY_TREATMENT = 'Discontinuity Treatment'
+LAG_SELECTION = 'Lag Selection'
+STATIC_WINDOW_REMOVAL = 'Static Window Removal'
+DEFINE_VARIABLES_LIMITS = 'Define Variables Limits'
+NORMALIZATION = 'Normalization'
+
 
 class LinearRegressionModel(BaseEstimator, TransformerMixin):
     """
@@ -218,11 +224,11 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
 
         # Filter steps for preprocessor class
         possible_steps = [
-            'Discontinuity Treatment',
-            'Lag Selection',
-            'Static Window Removal',
-            'Define Variables Limits',
-            'Normalization',
+            DISCONTINUITY_TREATMENT,
+            LAG_SELECTION,
+            STATIC_WINDOW_REMOVAL,
+            DEFINE_VARIABLES_LIMITS,
+            NORMALIZATION,
             'Feature Creation',
             'Lag Creation',
         ]
@@ -456,39 +462,36 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
 
         for step in self.steps_order:
             # Discontinuity Treatment
-            if step == 'Discontinuity Treatment':
+            if step == DISCONTINUITY_TREATMENT:
                 data_treat = self.treat_discontinuities(data_treat)
 
             # Lag for Model Training
-            if step == 'Lag Selection':
+            if step == LAG_SELECTION:
                 data_treat = self.lag_selection(data_treat, self.lag_train)
 
             # Static Window Treatment
-            if step == 'Static Window Removal':
+            if step == STATIC_WINDOW_REMOVAL:
                 data_treat = self.treat_static_windows(data_treat)
 
             # Adjust limits
-            if step == 'Define Variables Limits':
+            if step == DEFINE_VARIABLES_LIMITS:
                 data_treat = self.adjust_limits(data_treat)
 
             # Normalization
-            if step == 'Normalization':
-                if self.scaler:
-                    self.scaler = self.scaler.fit(data_treat[existing_columns])
-                    self.feature_names_order = list(data_treat[existing_columns].columns)
-                    data_treat[existing_columns] = self.scaler.transform(
-                        data_treat[existing_columns]
-                    )
+            if step == NORMALIZATION and self.scaler:
+                self.scaler = self.scaler.fit(data_treat[existing_columns])
+                self.feature_names_order = list(data_treat[existing_columns].columns)
+                data_treat[existing_columns] = self.scaler.transform(data_treat[existing_columns])
 
-                    # Save scaler parameters
-                    assert self.scaler_params is not None, 'scaler_params must be initialized'
-                    for index, column in enumerate(list(existing_columns)):
-                        mean = self.scaler.mean_[index]
-                        variance = self.scaler.var_[index]
-                        self.scaler_params[column] = {
-                            'mean': round(mean, 3),
-                            'variance': round(variance, 3),
-                        }
+                # Save scaler parameters
+                assert self.scaler_params is not None, 'scaler_params must be initialized'
+                for index, column in enumerate(list(existing_columns)):
+                    mean = self.scaler.mean_[index]
+                    variance = self.scaler.var_[index]
+                    self.scaler_params[column] = {
+                        'mean': round(mean, 3),
+                        'variance': round(variance, 3),
+                    }
 
         return self
 
@@ -515,28 +518,25 @@ class DataPreprocessor(BaseEstimator, TransformerMixin):
 
         for step in self.steps_order:
             # Discontinuity Treatment
-            if step == 'Discontinuity Treatment':
+            if step == DISCONTINUITY_TREATMENT:
                 data_treat = self.treat_discontinuities(data_treat)
 
             # Lag for Model Training
-            if step == 'Lag Selection':
+            if step == LAG_SELECTION:
                 data_treat = self.lag_selection(data_treat, self.lag_transform)
 
             # Static Window Treatment
-            if step == 'Static Window Removal':
+            if step == STATIC_WINDOW_REMOVAL:
                 data_treat = self.treat_static_windows(data_treat)
 
             # Adjust limits
-            if step == 'Define Variables Limits':
+            if step == DEFINE_VARIABLES_LIMITS:
                 data_treat = self.adjust_limits(data_treat)
 
             # Normalization
-            if step == 'Normalization':
-                if self.scaler:
-                    data_treat = data_treat[self.feature_names_order]
-                    data_treat[existing_columns] = self.scaler.transform(
-                        data_treat[existing_columns]
-                    )
+            if step == NORMALIZATION and self.scaler:
+                data_treat = data_treat[self.feature_names_order]
+                data_treat[existing_columns] = self.scaler.transform(data_treat[existing_columns])
 
             # Feature Creation
             if step == 'Feature Creation':