diff --git a/cnn_classification_service/classifier_cache.py b/cnn_classification_service/classifier_cache.py
index 887af21..f36f764 100644
--- a/cnn_classification_service/classifier_cache.py
+++ b/cnn_classification_service/classifier_cache.py
@@ -64,7 +64,8 @@ class ClassifierCache:
         self._downloaded_files.append(weights_file_path)
 
         # magic happens here
-        self._current_classifier = Classifier(model_file_path, weights_file_path)
+        with opentracing.tracer.start_active_span('classifier.loadNewClassifier'):
+            self._current_classifier = Classifier(model_file_path, weights_file_path)
 
     def get_default_classifier(self) -> Tuple[dict, Classifier]:
 
diff --git a/cnn_classification_service/cnn_classifier.py b/cnn_classification_service/cnn_classifier.py
index 4ced69c..041b7ad 100644
--- a/cnn_classification_service/cnn_classifier.py
+++ b/cnn_classification_service/cnn_classifier.py
@@ -18,14 +18,21 @@ from keras_preprocessing.image import ImageDataGenerator
 class Classifier(object):
 
     def __init__(self, model_filename: str, weights_filename: str):
-        with open(model_filename, 'r') as f:
-            self.loaded_model = model_from_json(f.read())
+        with opentracing.tracer.start_active_span('classifier.loadModel'):
+            with open(model_filename, 'r') as f:
+                self.loaded_model = model_from_json(f.read())
 
-        self.loaded_model.load_weights(weights_filename)
-        self.datagen = ImageDataGenerator(rescale=1. / 255., validation_split=0.25)
-        self.loaded_model.compile(optimizers.RMSprop(lr=0.0005, decay=1e-6), loss="categorical_crossentropy",
-                                  metrics=["accuracy"])
-        self.loaded_model.summary()
+        with opentracing.tracer.start_active_span('classifier.loadWeights'):
+            self.loaded_model.load_weights(weights_filename)
+
+        with opentracing.tracer.start_active_span('classifier.loadDatagen'):
+            self.datagen = ImageDataGenerator(rescale=1. / 255., validation_split=0.25)
+
+        with opentracing.tracer.start_active_span('classifier.compile'):
+            self.loaded_model.compile(optimizers.RMSprop(lr=0.0005, decay=1e-6), loss="categorical_crossentropy",
+                                      metrics=["accuracy"])
+
+        self.loaded_model.summary() # prints to stdout??
 
     @staticmethod
     def create_spectrogram(wav_filename: str) -> Tuple[str, str]: