Adapted new style; small rewrites

Author: GiovanniGiacometti

md-docs/stylesheets/extra.css

@@ -30,4 +30,8 @@
   background-color: rgb(43, 155, 70);
   -webkit-mask-image: var(--md-admonition-icon--code-block);
           mask-image: var(--md-admonition-icon--code-block);
+}
+
+.nice-list ul{
+    list-style-type: circle;
 }

md-docs/user_guide/data.md

@@ -19,7 +19,7 @@ Available categories are:
 The [Data Schema] created for the [Task] contains a list of Column objects, each of which has a _Role_.
 Naturally, there is a relationship between the Column's Role and the Data Category.
 In fact, each Data Category comprises a set of Column objects with certain Roles.
-So that, when you upload samples belonging to a Data Category, they must contains all the Columns objects declared on the Data Schema to be considered valid.
+When you upload samples belonging to a Data Category, they must contain all the Columns objects declared on the Data Schema to be considered valid.
 
 The following table shows these relationships:
 
@@ -130,7 +130,7 @@ For RAG Tasks, reference data can be used to indicate the type of data expected
     You can set reference data as follow:
 
     ``` py
-    job_id = job_id = client.set_model_reference(
+    job_id = client.set_model_reference(
         model_id=model_id,
         from_timestamp=from_timestamp,
         to_timestamp=to_timestamp,

md-docs/user_guide/modules/index.md

@@ -13,15 +13,15 @@ Modules can be always active or on-demand: Monitoring module and Drift Explainab
 
     Data drift detection over data.
 
-    [:octicons-arrow-right-24: More info](user_guide/company.md)
+    [:octicons-arrow-right-24: More info](../monitoring/index.md)
 
 -   :material-compare:{ .lg .middle } **Drift Explainability**
 
     ---
 
     Understand the nature of detected drift.
 
-    [:octicons-arrow-right-24: More info](user_guide/modules/index.md)
+    [:octicons-arrow-right-24: More info](../monitoring/drift_explainability.md)
 
 -   :material-speedometer:{ .lg .middle } **Retraining**
 

md-docs/user_guide/monitoring/detection_event.md

@@ -5,13 +5,17 @@ A Detection Event is raised by the ML cube Platform when a significant change is
 An event is characterized by the following attributes:
 
 - `Event Type`: the type of the event. It's possible values are:
-  - `Warning On`: the monitoring entity is experiencing slight changes that might lead to a drift.
-  - `Warning Off`: the monitoring entity has returned to the reference distribution. 
-  - `Drift On`: the monitoring entity has drifted from the reference distribution.
-  - `Drift Off`: the monitoring entity has returned to the reference distribution.
+        <div class="nice-list">
+            <ul>
+                <li> `Warning On`: the monitoring entity is experiencing slight changes that might lead to a drift.</li>
+                <li> `Warning Off`: the monitoring entity has returned to the reference distribution. </li>
+                <li> `Drift On`: the monitoring entity has drifted from the reference distribution.</li>
+                <li> `Drift Off`: the monitoring entity has returned to the reference distribution.</li>
+            </ul>
+        </div>
 - `Severity`: the severity of the event. It's provided only for drift events and it can be `Low`, `Medium`, or `High`.
-- `Monitoring Target`: the Monitoring Target being monitored (see the [Monitoring] page for more details on what a Monitoring Target is).
-- `Monitoring Metric`: the Monitoring Metric being monitored (see the [Monitoring] page for more details on what a Monitoring Metric is).
+- `Monitoring Target`: the [Monitoring Target](index.md#monitoring-metrics) being monitored.
+- `Monitoring Metric`: the [Monitoring Metric](index.md#monitoring-metrics) being monitored.
 - `Model Name`: the name of the model that raised the event. It's present only if the event is related to a model.
 - `Model Version`: the version of the model that raised the event. It's present only if the event is related to a model.
 - `Insert datetime`: the time when the event was raised.

md-docs/user_guide/monitoring/detection_event_rules.md

@@ -9,9 +9,9 @@ Rules are specific to a task and are characterized by the following attributes:
 
 - `Name`: a descriptive label of the rule.
 - `Detection Event Type`: the type of event that triggers the rule.
-- `Severity`: the severity of the event that triggers the rule. If not specified, the rule will be triggered by events of any severity.
-- `Monitoring Target`: the monitoring target whose event should trigger the rule. 
-- `Monitoring Metric`: the monitoring metric whose event should trigger the rule.
+- `Severity`: the severity of the event that triggers the rule. It is only applicable to drift events. If not specified, the rule will be triggered by drift events of any severity.
+- `Monitoring Target`: the [Monitoring Target](index.md#monitoring-targets) whose event should trigger the rule. 
+- `Monitoring Metric`: the [Monitoring Metric](index.md#monitoring-metrics) whose event should trigger the rule.
 - `Model name`: the name of the model to which the rule applies. This is only required when the monitoring target is related to a model
   (such as `ERROR` or `PREDICTION`).
 - `Actions`: A list of actions to be executed sequentially when the rule is triggered.

md-docs/user_guide/monitoring/drift_explainability.md

@@ -6,7 +6,7 @@ ensuring the model continues to function as expected. However, monitoring only i
 In order to make the right decisions, you need to understand what were the main factors that led to the drift in the first place, so that
 the correct actions can be taken to mitigate it.
 
-The ML cube Platform supports this process by offering what we refer to as "**Drift Explainability Reports**", 
+The ML cube Platform supports this process by offering what we refer to as **Drift Explainability Reports**, 
 automatically generated upon the detection of a drift and containing several elements that should help you diagnose the root causes 
 of the change occurred.
 
@@ -20,7 +20,7 @@ This is because the elements of the report needs a significant number of samples
 If the distribution moves back to the reference before enough samples are collected, the report might not be generated.
 
 Each report is composed of several entities, each providing a different perspective on the data and the drift occurred. 
-Most of them are specific to a certain [Data Structure], so they might not be available for all tasks.
+Most of them are specific to a certain `Data Structure`, so they might not be available for all tasks.
 
 These entities can take the form of tables, plots, or textual explanations. 
 Observed and analyzed together, they should provide a comprehensive understanding of the drift and its underlying causes.
@@ -35,4 +35,3 @@ These are the entities currently available:
  separating power. This entity is available only for tasks with tabular data.
 
 [Monitoring]: index.md
-[Data Structure]: ../../api/python/enums.md#datastructure