Drift Explainability Report Images

Author: GiovanniGiacometti

md-docs/imgs/monitoring/drift-explainability/concept-fi.svg

@@ -10,14 +10,14 @@ The ML cube Platform supports this process by offering what we refer to as **Dri
 automatically generated upon the detection of a drift and containing several elements that should help you diagnose the root causes 
 of the change occurred.
 
-You can access the reports by navigating to the `Drift Explainability` tab in the sidebar of the task page.
+You can access the reports in the WebApp, by navigating to the `Drift Explainability` tab in the sidebar of the Task page.
 
 ## Structure
 
 A Drift Explainability Report consists in comparing the reference data and the portion of production data where the drift was identified, hence 
-those belonging to the new data distribution. Notice that these reports are generated after a sufficient amount of samples has been collected after the drift.
-This is because the elements of the report needs a significant number of samples to guarantee statistical reliability of the results.
-If the distribution moves back to the reference before enough samples are collected, the report might not be generated.
+those belonging to the new data distribution. Notice that these reports are generated after a sufficient amount of samples has been collected 
+after the drift, in order to ensure statistical reliability of the results.
+If the data 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.
@@ -29,9 +29,36 @@ These are the entities currently available:
 - `Feature Importance`: it's a barplot that illustrates how the significance of each feature differs between the reference 
  and the production datasets. Variations in a feature's values might suggest that its contribution to the model's predictions 
  has changed over time. This entity is available only for tasks with tabular data.
+
+<figure markdown>
+  ![Feature Importance](../../imgs/monitoring/drift-explainability/fi.svg)
+  <figcaption>Example of a feature importance plot.</figcaption>
+</figure>
+
 - `Variable discriminative power`: it's also a bar plot displays the influence of each feature, as well as the target, 
  in differentiating between the reference and the production datasets. 
  The values represent how strongly a given feature helps to distinguish the datasets, with higher values representing stronger 
  separating power. This entity is available only for tasks with tabular data.
 
+<figure markdown>
+  ![Variable discriminative power](../../imgs/monitoring/drift-explainability/concept-fi.svg)
+  <figcaption>Example of a variable discriminative power plot.</figcaption>
+</figure>
+
+- `Drift Score`: it's a line plot that shows the evolution of the drift score over time. The drift score is a 
+  measure of the statistical distance between a sliding window of the production data and the reference data. It also shows the threshold,
+  which is the value that the drift score must exceed to raise a drift alarm, and all the [Detection Events] that were triggered in
+  the time frame of the report. This plot helps in understanding how the drift evolved over time and the moments in which the difference
+  between the two datasets was higher. Notice that some postprocessing is applied on the events to account for the functioning of the drift detection algorithms. 
+  Specifically,
+  we shift back the drift on events by a certain offset, aiming to point at the precise time when the drift actually started. As a result,
+  drift on events might be shown before the threshold is exceeded. This entity is available for all tasks.
+
+
+<figure markdown style="width: 100%">
+  ![Drift score](../../imgs/monitoring/drift-explainability/score.svg)
+  <figcaption style="width: 100%; text-align: center;">Example of a drift score plot with detection events of increasing severity displayed.</figcaption>
+</figure>
+
 [Monitoring]: index.md
+[Detection Events]: detection_event.md

md-docs/imgs/monitoring/drift-explainability/fi.svg

@@ -50,7 +50,10 @@ is automatically enabled upon the task creation. The idea underlying defining ma
 only the model error, is to provide a comprehensive view of the model's
 performance and the data distribution, easing the identification of the root causes of a drift and thus facilitating the corrective actions.
 
-![Monitoring Targets and Metrics overview](../../imgs/monitoring-overview.svg)
+<figure markdown style="width: 100%">
+  ![Monitoring Overview](../../imgs/monitoring-overview.svg)
+  <figcaption> Monitoring Targets and Metrics overview</figcaption>
+</figure>
 
 #### Monitoring Targets