diff --git a/src/image_prediction/default_objects.py b/src/image_prediction/default_objects.py
index d66d477..1682e74 100644
--- a/src/image_prediction/default_objects.py
+++ b/src/image_prediction/default_objects.py
@@ -1,4 +1,5 @@
 from typing import Iterable
+import logging
 
 from funcy import juxt
 
@@ -19,8 +20,11 @@ from image_prediction.transformer.transformers.coordinate.pdfnet import PDFNetCo
 from image_prediction.transformer.transformers.response import ResponseTransformer
 from pdf2img.extraction import extract_images_via_metadata
 
+logger = logging.getLogger(__name__)
+
 
 def get_mlflow_model_loader(mlruns_dir):
+    logger.debug("fetching model loader")
     model_loader = ModelLoader(MlflowConnector(MlflowModelReader(mlruns_dir)))
     return model_loader
 
diff --git a/src/image_prediction/pipeline.py b/src/image_prediction/pipeline.py
index d69e453..61d5b99 100644
--- a/src/image_prediction/pipeline.py
+++ b/src/image_prediction/pipeline.py
@@ -1,4 +1,5 @@
 import os
+import logging
 from functools import partial
 from itertools import chain, tee
 from typing import Iterable
@@ -17,14 +18,18 @@ from image_prediction.utils.generic import lift, starlift
 
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
+logger = logging.getLogger(__name__)
+
 
 def load_model(mlruns_dir, config):
+    logger.debug("loading model")
     model_loader = get_mlflow_model_loader(mlruns_dir)
     model_identifier = config.service.mlflow_run_id
     return get_image_classifier(model_loader, model_identifier)
 
 
 def load_pipeline(**kwargs):
+    logger.debug("loading pipeline")
     return Pipeline(**kwargs)
 
 
@@ -38,6 +43,7 @@ def star(f):
 
 class Pipeline:
     def __init__(self, model, batch_size=16, verbose=True, **kwargs):
+        logger.debug("Pipeline class instantiated")
         self.verbose = verbose
 
         extract = get_dispatched_extract(**kwargs)
diff --git a/src/serve.py b/src/serve.py
index de94f03..51caf8e 100644
--- a/src/serve.py
+++ b/src/serve.py
@@ -40,12 +40,14 @@ def process_request(request_message, bucket=BUCKET, storage=STORAGE, model=MODEL
     pipeline = load_pipeline(model=model, verbose=img_config.service.verbose, batch_size=img_config.service.batch_size)
 
     if storage.exists(bucket, target_file_name):
+        logger.debug("processing target_file_name %s", target_file_name)
         should_publish_result = True
         object_bytes = storage.get_object(bucket, target_file_name)
         object_bytes = gzip.decompress(object_bytes)
         classifications = list(pipeline(pdf=object_bytes))
 
         if storage.exists(bucket, figure_data_file_name):
+            logger.debug("processing figure_data_file_name %s", figure_data_file_name)
             metadata_bytes = storage.get_object(bucket, figure_data_file_name)
             metadata_bytes = gzip.decompress(metadata_bytes)
             metadata_per_image = json.load(io.BytesIO(metadata_bytes))["data"]
@@ -54,6 +56,7 @@ def process_request(request_message, bucket=BUCKET, storage=STORAGE, model=MODEL
             classifications_cv = []
 
         result = {**request_message, "data": classifications, "dataCV": classifications_cv}
+        logger.debug("result message:\n%s", result)
         storage_bytes = gzip.compress(json.dumps(result).encode("utf-8"))
         storage.put_object(bucket, response_file_name, storage_bytes)
     else: