Fixed broken links in Module 4

docs/book/README.md

@@ -43,7 +43,7 @@ ML observability course is organized into six modules. You can follow the comple
 [Module 3. ML monitoring for unstructured data: NLP, LLM, and embeddings](ml-observability-course/module-3-ml-monitoring-for-unstructured-data/readme.md). 
 {% endcontent-ref %}
 
-{% content-ref url="ml-observability-course/module-4-designing-effective-ml-monitoring.md" %}
+{% content-ref url="ml-observability-course/module-4-designing-effective-ml-monitoring/readme.md" %}
 [Module 4. Designing effective ML monitoring](ml-observability-course/module-4-designing-effective-ml-monitoring/readme.md). 
 {% endcontent-ref %}
 

docs/book/ml-observability-course/module-4-designing-effective-ml-monitoring/choosing-ml-monitoring-deployment-architecture.md

@@ -96,4 +96,5 @@ We discussed the differences between different ML monitoring architectures. Here
 ## Enjoyed the content?
 
 Star Evidently on GitHub to contribute back! This helps us create free, open-source tools and content for the community.
+
 ⭐️ [Star](https://github.com/evidentlyai/evidently) on GitHub!

docs/book/ml-observability-course/module-4-designing-effective-ml-monitoring/how-to-prioritize-monitoring-metrics.md

@@ -75,7 +75,7 @@ An important thing to add is monitoring the number of model predictions. This is
 
 ![](<../../../images/2023110\_course\_module4\_fin.025-min.png>)
 
-**2. Model performance **
+**2. Model performance**
 
 This set of metrics answers the question "How does the service perform?" and “Did anything break?”