diff --git a/Web-App/1-Web-App/README.md b/Web-App/1-Web-App/README.md
index e1608b0d..1f7c2fdd 100644
--- a/Web-App/1-Web-App/README.md
+++ b/Web-App/1-Web-App/README.md
@@ -263,11 +263,16 @@ First, dependencies are loaded and the app starts. Then, the model is imported.
 
 Using a model this way, with Flask and a pickled model, is relatively straightforward. The hardest thing is to understand what shape the data is that must be sent to the model to get a prediction. That all depends on how the model was trained. This one has three data points to be input in order to get a prediction. In a professional setting, you can see how good communication is necessary between the folks who train the model and those who consume it in a web or mobile app. In our case, it's only one person, you!
 
-🚀 Challenge: Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
+## 🚀 Challenge: 
+
+Instead of working in a notebook and importing the model to the Flask app, you could train the model right within the Flask app! Try converting your Python code in the notebook, perhaps after your data is cleaned, to train the model from within the app on a route called `train`. What are the pros and cons of pursuing this method?
+
 ## [Post-lecture quiz](link-to-quiz-app)
 
 ## Review & Self Study
 
+There are many ways to build a web app to consume ML models. Make a list of the ways you could use JavaScript or Python to build a web app to leverage machine learning. Consider architecture: should the model stay in the app or live in the cloud? If the latter, how would you access it? Draw out an architectural model for an applied ML web solution.
+
 **Assignment**: [Try a different model](assignment.md)