diff --git a/examples/pipeline/pipeline_from_decorator.py b/examples/pipeline/pipeline_from_decorator.py
index 0d7a065e..a37baeb4 100644
--- a/examples/pipeline/pipeline_from_decorator.py
+++ b/examples/pipeline/pipeline_from_decorator.py
@@ -26,8 +26,10 @@ def step_one(pickle_data_url: str, extra: int = 43):
 # notice all package imports inside the function will be automatically logged as
 # required packages for the pipeline execution step.
 # Specifying `return_values` makes sure the function step can return an object to the pipeline logic
-# In this case, the returned tuple will be stored as an artifact named "processed_data"
-@PipelineDecorator.component(return_values=['processed_data'], cache=True, task_type=TaskTypes.data_processing)
+# In this case, the returned tuple will be stored as an artifact named "X_train, X_test, y_train, y_test"
+@PipelineDecorator.component(
+    return_values=['X_train, X_test, y_train, y_test'], cache=True, task_type=TaskTypes.data_processing
+)
 def step_two(data_frame, test_size=0.2, random_state=42):
     print('step_two')
     # make sure we have pandas for this step, we need it to use the data_frame
@@ -36,7 +38,8 @@ def step_two(data_frame, test_size=0.2, random_state=42):
     y = data_frame['target']
     X = data_frame[(c for c in data_frame.columns if c != 'target')]
     X_train, X_test, y_train, y_test = train_test_split(
-        X, y, test_size=test_size, random_state=random_state)
+        X, y, test_size=test_size, random_state=random_state
+    )
 
     return X_train, X_test, y_train, y_test
 
@@ -47,21 +50,26 @@ def step_two(data_frame, test_size=0.2, random_state=42):
 # Specifying `return_values` makes sure the function step can return an object to the pipeline logic
 # In this case, the returned object will be stored as an artifact named "model"
 @PipelineDecorator.component(return_values=['model'], cache=True, task_type=TaskTypes.training)
-def step_three(data):
+def step_three(X_train, y_train):
     print('step_three')
     # make sure we have pandas for this step, we need it to use the data_frame
     import pandas as pd  # noqa
     from sklearn.linear_model import LogisticRegression
-    from clearml import Task
-    X_train, X_test, y_train, y_test = data
     model = LogisticRegression(solver='liblinear', multi_class='auto')
     model.fit(X_train, y_train)
-    score = model.score(X_test,y_test)
-    # Get current step's Task
-    task = Task.current_task()
-    task.get_logger().report_single_value(name='accuracy',value=score)
     return model
 
+# Make the following function an independent pipeline component step
+# notice all package imports inside the function will be automatically logged as
+# required packages for the pipeline execution step
+# Specifying `return_values` makes sure the function step can return an object to the pipeline logic
+# In this case, the returned object will be stored as an artifact named "accuracy"
+@PipelineDecorator.component(return_values=['accuracy'], cache=True, task_type=TaskTypes.qc)
+def step_four(model, X_data, Y_data):
+    from sklearn.linear_model import LogisticRegression  # noqa
+    from sklearn.metrics import accuracy_score
+    Y_pred = model.predict(X_data)
+    return accuracy_score(Y_data, Y_pred, normalize=True)
 
 # The actual pipeline execution context
 # notice that all pipeline component function calls are actually executed remotely
@@ -80,14 +88,21 @@ def executing_pipeline(pickle_url, mock_parameter='mock'):
     # When actually passing the `data_frame` object into a new step,
     # It waits for the creating step/function (`step_one`) to complete the execution
     print('launch step two')
-    processed_data = step_two(data_frame)
+    X_train, X_test, y_train, y_test = step_two(data_frame)
 
     print('launch step three')
-    model = step_three(processed_data)
+    model = step_three(X_train, y_train)
 
     # Notice since we are "printing" the `model` object,
     # we actually deserialize the object from the third step, and thus wait for the third step to complete.
-    print('pipeline completed with model: {}'.format(model))
+    print('returned model: {}'.format(model))
+
+    print('launch step four')
+    accuracy = 100 * step_four(model, X_data=X_test, Y_data=y_test)
+
+    # Notice since we are "printing" the `accuracy` object,
+    # we actually deserialize the object from the fourth step, and thus wait for the fourth step to complete.
+    print(f"Accuracy={accuracy}%")
 
 
 if __name__ == '__main__':