diff --git a/5-Data-Science-In-Cloud/18-tbd/assignment.md b/5-Data-Science-In-Cloud/18-tbd/assignment.md
index b7af6412..9dbc5a2a 100644
--- a/5-Data-Science-In-Cloud/18-tbd/assignment.md
+++ b/5-Data-Science-In-Cloud/18-tbd/assignment.md
@@ -1,8 +1,11 @@
-# Title
+# [Low code/No code] The Heart Failure Prediction Project
 
 ## Instructions
 
+We saw how to use the Azure ML platform to train, deploy and consume a model in a Low code/No code fashion. Now look around for some data that you could use to train an other model, deploy it and consume it. You can look for datasets on [Kaggle](https://kaggle.com) and [Azure Open Datasets](https://azure.microsoft.com/en-us/services/open-datasets/catalog/?WT.mc_id=academic-15963-cxa).
+
 ## Rubric
 
-Exemplary | Adequate | Needs Improvement
---- | --- | -- |
+| Exemplary | Adequate | Needs Improvement |
+|-----------|----------|-------------------|
+|When uploading the data you took care of changing the feature's type if necessary. You also cleaned the data if needed. You ran a training on a dataset through AutoML, and you checked the model explanations. You deployed the best model and you were able to consume it. | When uploading the data you took care of changing the feature's type if necessary. You ran a training on a dataset through AutoML, you deployed the best model and you were able to consume it. | You have deployed the best model trained by AutoML and you were able to consume it. |