diff --git a/modules/ml-pipeline/src/pipeline/0_startup_cleanup.py b/modules/ml-pipeline/src/pipeline/0_startup_cleanup.py
index 0bfa37f..32e8a1b 100644
--- a/modules/ml-pipeline/src/pipeline/0_startup_cleanup.py
+++ b/modules/ml-pipeline/src/pipeline/0_startup_cleanup.py
@@ -16,13 +16,9 @@ def run_cleanup(artefacts_directory: str, metrics_directory: str) -> None:
     Remove the directory where artefacts are stored
     """
 
-    logger.info("---------------------")
     logger.info(f"--- Run Clean up ---")
-    logger.info("---------------------")
 
-    logger.info("-------------------------")
     logger.info(f"--- Delete artefacts ---")
-    logger.info("-------------------------")
 
     artefact_directory_path = Path(artefacts_directory)
 
@@ -31,9 +27,7 @@ def run_cleanup(artefacts_directory: str, metrics_directory: str) -> None:
         logger.info(f"Removing the directory: {artefacts_directory}")
         shutil.rmtree(artefact_directory_path)
 
-    logger.info("-----------------------")
     logger.info(f"--- Delete metrics ---")
-    logger.info("-----------------------")
 
     metrics_directory_path = Path(metrics_directory)
 
@@ -45,15 +39,11 @@ def run_cleanup(artefacts_directory: str, metrics_directory: str) -> None:
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
     run_cleanup(
         artefacts_directory=startup_cleanup_params["artefacts"],
         metrics_directory=startup_cleanup_params["metrics"],
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")
diff --git a/modules/ml-pipeline/src/pipeline/1_prepare_data.py b/modules/ml-pipeline/src/pipeline/1_prepare_data.py
index 32daa19..ed7e057 100644
--- a/modules/ml-pipeline/src/pipeline/1_prepare_data.py
+++ b/modules/ml-pipeline/src/pipeline/1_prepare_data.py
@@ -17,9 +17,7 @@ from core.DataClient import dataclient_factory
 from core.FeatureProcessor import feature_processor_factory
 from config import settings
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate Parameters ---")
-logger.info("----------------------------")
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
 
@@ -33,9 +31,7 @@ output_train_filepath = prepare_data_params["output_train_filepath"]
 output_test_filepath = prepare_data_params["output_test_filepath"]
 feature_processor_config = feature_process_params["feature_processor_config"]
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate DataClient ---")
-logger.info("----------------------------")
 
 input_dataclient_type = prepare_data_params["input_dataclient_type"]
 output_dataclient_type = prepare_data_params["output_dataclient_type"]
@@ -49,9 +45,7 @@ output_dataclient = dataclient_factory(
     dataclient_config=client_params[output_dataclient_type],
 )
 
-logger.info("----------------------------------")
 logger.info(f"--- Initiate FeatureProcessor ---")
-logger.info("----------------------------------")
 
 feature_processor = feature_processor_factory(
     feature_process_params["feature_processor_type"]
@@ -76,15 +70,11 @@ def prepare_data(
     :param pipeline_mode: bool, Default False, this caches out the file for experimentation, objects returned in pipeline mode
     """
 
-    logger.info("--------------------")
     logger.info("--- Loading data ---")
-    logger.info("--------------------")
 
     data = input_dataclient.load_data(location=data_filepath, load_config={})
 
-    logger.info("--------------------------")
     logger.info("--- Feature Processing ---")
-    logger.info("--------------------------")
 
     data = feature_processor.feature_process(
         data,
@@ -93,9 +83,7 @@ def prepare_data(
         new_feature_funcs=new_feature_funcs,
     )
 
-    logger.info("----------------------")
     logger.info("--- Splitting data ---")
-    logger.info("----------------------")
 
     if train_proportion == 1:
         train = data
@@ -108,9 +96,7 @@ def prepare_data(
 
     train = train.reset_index(drop=True)
 
-    logger.info("-----------------------")
     logger.info("--- Outputting data ---")
-    logger.info("-----------------------")
 
     output_dataclient.save_data(
         obj=train, location=output_train_filepath, save_config=None
@@ -126,13 +112,9 @@ def prepare_data(
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
-    logger.info("---------------------------")
     logger.info(f"--- Prepare Data Stage ---")
-    logger.info("---------------------------")
 
     prepare_data(
         input_dataclient=input_dataclient,
@@ -147,6 +129,4 @@ if __name__ == "__main__":
         new_feature_funcs=new_feature_funcs,
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")
diff --git a/modules/ml-pipeline/src/pipeline/2_build_model.py b/modules/ml-pipeline/src/pipeline/2_build_model.py
index cae5cfd..f3504a7 100644
--- a/modules/ml-pipeline/src/pipeline/2_build_model.py
+++ b/modules/ml-pipeline/src/pipeline/2_build_model.py
@@ -18,9 +18,7 @@ from core.MLMetrics import metrics_factory
 from configs.post_prediction_logic import post_prediction_logic
 from config import settings
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate Parameters ---")
-logger.info("----------------------------")
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
 
@@ -40,22 +38,16 @@ train_filepath = prepare_data_params["output_train_filepath"]
 test_filepath = prepare_data_params["output_test_filepath"]
 fit_metrics_filepath = build_model_params["fit_metrics_filepath"]
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate DataClient ---")
-logger.info("----------------------------")
 
 # Output of previous prepare data step, will be where the data is
 dataclient = dataclient_factory(prepare_data_params["output_dataclient_type"])
 
-logger.info("-------------------------")
 logger.info(f"--- Initiate MLModel ---")
-logger.info("-------------------------")
 
 model = model_factory(model_type)
 
-logger.info("-------------------------")
 logger.info(f"--- Initiate Metrics ---")
-logger.info("-------------------------")
 
 metrics = metrics_factory(generate_metrics_params["metrics_type"])
 
@@ -75,9 +67,8 @@ def build_model(
     test_data: Union[pd.DataFrame, None] = None,
     pipeline_mode: bool = False,
 ):
-    logger.info("--------------------------------------")
+
     logger.info("--- Loading Data for build process ---")
-    logger.info("--------------------------------------")
 
     if train_data is None:
         if train_filepath is None:
@@ -89,9 +80,7 @@ def build_model(
             raise ValueError(f"Need {test_filepath} if no data supplied")
         test_data = dataclient.load_data(location=test_filepath, load_config=None)
 
-    logger.info("----------------------")
     logger.info("--- Training model ---")
-    logger.info("----------------------")
 
     model.train_model(
         data=train_data.drop(columns=identifier_columns),
@@ -99,32 +88,24 @@ def build_model(
         model_hyperparameters=model_hyperparameters,
     )
 
-    logger.info("----------------------------------")
     logger.info("--- Generating fit predictions ---")
-    logger.info("----------------------------------")
 
     fit_predictions = model.predict(
         data=train_data, post_prediction_logic=post_prediction_logic
     )
 
-    logger.info("------------------------------")
     logger.info("--- Generating fit metrics ---")
-    logger.info("------------------------------")
 
     metrics_output = metrics.generate_metrics(
         target=train_data[target],
         predictions=pd.Series(fit_predictions),
     )
 
-    logger.info("--------------------")
     logger.info("--- Saving model ---")
-    logger.info("--------------------")
 
     model.save_model(path=Path(model_save_location))
 
-    logger.info("--------------------------")
     logger.info("--- Saving fit metrics ---")
-    logger.info("--------------------------")
 
     dataclient.save_data(
         obj=metrics_output, location=fit_metrics_filepath, save_config=None
@@ -133,13 +114,9 @@ def build_model(
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
-    logger.info("--------------------------")
     logger.info(f"--- Build Model Stage ---")
-    logger.info("--------------------------")
 
     build_model(
         dataclient=dataclient,
@@ -154,6 +131,4 @@ if __name__ == "__main__":
         fit_metrics_filepath=fit_metrics_filepath,
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")
diff --git a/modules/ml-pipeline/src/pipeline/3_generate_predictions.py b/modules/ml-pipeline/src/pipeline/3_generate_predictions.py
index 9461392..acb9e99 100644
--- a/modules/ml-pipeline/src/pipeline/3_generate_predictions.py
+++ b/modules/ml-pipeline/src/pipeline/3_generate_predictions.py
@@ -10,9 +10,7 @@ from core.Logger import logger
 from config import settings
 from generate_predictions import generate_predictions
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate Parameters ---")
-logger.info("----------------------------")
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
 
@@ -33,15 +31,11 @@ model_filepath = build_model_params["model_save_filepath"]
 predictions_output_filepath = generate_predictions_params["predictions_output_filepath"]
 predictions_column_name = generate_predictions_params["predictions_column_name"]
 
-logger.info("-------------------------")
 logger.info(f"--- Initiate MLModel ---")
-logger.info("-------------------------")
 
 model = model_factory(build_model_params["model_type"])
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate DataClient ---")
-logger.info("----------------------------")
 
 # We may have different locations of loading hence why we use one specified in generate_predictions.yaml
 # I.e. for metric runs, this will be a local data client
@@ -59,13 +53,9 @@ output_dataclient = dataclient_factory(
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
-    logger.info("----------------------------------")
     logger.info(f"--- Generate Predictions Stage---")
-    logger.info("----------------------------------")
 
     generate_predictions(
         input_dataclient=input_dataclient,
@@ -78,6 +68,4 @@ if __name__ == "__main__":
         predictions_column_name=predictions_column_name,
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")
diff --git a/modules/ml-pipeline/src/pipeline/4_generate_metrics.py b/modules/ml-pipeline/src/pipeline/4_generate_metrics.py
index 7b115a2..ddcd3cc 100644
--- a/modules/ml-pipeline/src/pipeline/4_generate_metrics.py
+++ b/modules/ml-pipeline/src/pipeline/4_generate_metrics.py
@@ -16,9 +16,7 @@ from core.MLMetrics import metrics_factory
 from core.Logger import logger
 from config import settings
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate Parameters ---")
-logger.info("----------------------------")
 
 RUNTIME_ENVIRONMENT = os.environ.get("RUNTIME_ENVIRONMENT", "local")
 
@@ -35,16 +33,11 @@ predictions_output_filepath = generate_predictions_params["predictions_output_fi
 predictions_column_name = generate_predictions_params["predictions_column_name"]
 metrics_output_filepath = generate_metrics_params["metrics_output_filepath"]
 
-
-logger.info("-------------------------")
 logger.info(f"--- Initiate MLModel ---")
-logger.info("-------------------------")
 
 model = model_factory(build_model_params["model_type"])
 
-logger.info("----------------------------")
 logger.info(f"--- Initiate DataClient ---")
-logger.info("----------------------------")
 
 # Use data client for input and output, as we use dvc to cache later to the cloud
 dataclient_type = generate_metrics_params["dataclient_type"]
@@ -53,9 +46,7 @@ dataclient = dataclient_factory(
     dataclient_config=client_params[dataclient_type],
 )
 
-logger.info("---------------------------")
 logger.info(f"--- Initiate MLMetrics ---")
-logger.info("---------------------------")
 
 metrics = metrics_factory(generate_metrics_params["metrics_type"])
 
@@ -75,34 +66,26 @@ def generate_metrics(
     For a given model, we generate prediction and evaluate this against the true target
     """
 
-    logger.info("-------------------------")
     logger.info("--- Loading test data ---")
-    logger.info("-------------------------")
 
     test_data = input_dataclient.load_data(
         location=test_data_filepath, load_config=None
     )
 
-    logger.info("---------------------------")
     logger.info("--- Loading predictions ---")
-    logger.info("---------------------------")
 
     predictions = input_dataclient.load_data(
         location=predictions_output_filepath, load_config=None
     )
 
-    logger.info("--------------------------")
     logger.info("--- Generating metrics ---")
-    logger.info("--------------------------")
 
     metrics_output = metrics.generate_metrics(
         target=test_data[target],
         predictions=pd.Series(predictions[predictions_column_name]),
     )
 
-    logger.info("----------------------")
     logger.info("--- Saving metrics ---")
-    logger.info("----------------------")
 
     output_dataclient.save_data(
         obj=metrics_output, location=metrics_output_filepath, save_config=None
@@ -111,13 +94,9 @@ def generate_metrics(
 
 if __name__ == "__main__":
 
-    logger.info("----------------------------")
     logger.info(f"--- {__file__} - Start! ---")
-    logger.info("----------------------------")
 
-    logger.info("------------------------------")
     logger.info(f"--- Generate Metrics Stage---")
-    logger.info("------------------------------")
 
     generate_metrics(
         input_dataclient=dataclient,
@@ -131,6 +110,4 @@ if __name__ == "__main__":
         metrics_output_filepath=metrics_output_filepath,
     )
 
-    logger.info("-------------------------------")
     logger.info(f"--- {__file__} - Complete! ---")
-    logger.info("-------------------------------")
diff --git a/modules/ml-pipeline/src/pipeline/core/Logger.py b/modules/ml-pipeline/src/pipeline/core/Logger.py
index a0fc231..2194063 100644
--- a/modules/ml-pipeline/src/pipeline/core/Logger.py
+++ b/modules/ml-pipeline/src/pipeline/core/Logger.py
@@ -22,6 +22,8 @@ def setup_logger():
     # Add the stream handler to the logger
     logger.addHandler(stream_handler)
 
+    logger.propagate = False
+
     return logger