Quick fixes post pr

Author: GiovanniGiacometti

md-docs/user_guide/monitoring/detection_event.md

@@ -1,6 +1,6 @@
 # Detection Event
 
-A [Detection Event] is raised by the MLCube Platform when a significant change is detected in one of the entities being monitored.
+A [Detection Event] is raised by the ML cube Platform when a significant change is detected in one of the entities being monitored.
 
 An event is characterized by the following attributes:
 
@@ -10,8 +10,8 @@ An event is characterized by the following attributes:
 the criticality of the detected drift.
 - `monitoring_target`: the [MonitoringTarget] being monitored.
 - `monitoring_metric`: the [MonitoringMetric] that triggered the event, if the event is related to a metric.
-- `model_name`: the name of the model that raised the event.
-- `model_version`: the version of the model that raised the event.
+- `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.
 - `sample_timestamp`: the timestamp of the sample that triggered the event.
 - 'sample_customer_id': the id of the customer that triggered the event.
@@ -23,7 +23,8 @@ You can access the detection events generated by the Platform in two ways:
 
 - **SDK**: the [get_detection_events] method can be used to retrieve all detection events for a specific task programmatically.
 - **WebApp**: navigate to the **`Detection `** section located in the task page's sidebar. Here, all detection events are displayed in a table, 
-   with multiple filtering options available for useful event management.
+   with multiple filtering options available for useful event management. Additionally, the latest detection events identified are shown in the Task homepage,
+   in the section named "Latest Detection Events".
 
 ## User Feedback
 

md-docs/user_guide/monitoring/detection_event_rules.md

@@ -2,7 +2,7 @@
 
 This section outlines how to configure automation to receive notifications or start retraining after a [Detection Event] occurs.
 
-When a detection event is produced, the MLCube Platform reviews all the detection event rules you have set 
+When a detection event is produced, the ML cube Platform reviews all the detection event rules you have set 
 and triggers those matching the event.
 
 Rules are specific to a task and require the following parameters:

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 MLCube 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.
 
@@ -16,6 +16,7 @@ You can access the reports by navigating to the `Drift Explainability` tab in th
 
 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 concept. 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.
 
 Each report is composed of several entities, each providing a different perspective on the data and the drift occurred. 

md-docs/user_guide/monitoring/index.md

@@ -15,9 +15,9 @@ in turn can have a negative impact on the business.
 Monitoring, also known as __Drift Detection__ in the literature, refers the process of continuously tracking the performance of a model 
 and the distribution of the data it is operating on. 
 
-## How does the MLCube Platform perform Monitoring?
+## How does the ML cube Platform perform Monitoring?
 
-The MLCube platform performs monitoring by employing statistical techniques to compare a certain reference (for instance, data used for training or the performance of a model
+The ML cube platform performs monitoring by employing statistical techniques to compare a certain reference (for instance, data used for training or the performance of a model
 on the test set) to incoming production data. If a significant difference is detected, an alarm is raised, signaling that the monitored entity
 is drifting away from the expected behavior and that corrective actions should be taken.
 
@@ -34,14 +34,15 @@ and the [Detection Event Rule] sections respectively.
 ### Targets and Metrics
 
 After explaining why monitoring is so important in modern AI systems and detailing how it is performed in the ML cube Platform, 
-we can introduce the concepts of Monitoring Targets and Monitoring Metrics. They both represent quantities that the MLCube Platform monitors, but they differ in their nature.
+we can introduce the concepts of Monitoring Targets and Monitoring Metrics. They both represent quantities that the ML cube Platform monitors, but they differ in their nature.
+They are both automatically defined by the ML cube platform based on the [Task] attributes, such as the Task type and the data structure.
 
 #### Monitoring Targets
 
 A Monitoring Target is a relevant entity involved in a [Task]. They represent the main quantities monitored by the platform, those whose
 variation can have a significant impact on the AI task success. 
 
-The MLCube platform supports the following monitoring targets:
+The ML cube platform supports the following monitoring targets:
 
 - `INPUT`: the input distribution, $P(X)$.
 - `CONCEPT`: the joint distribution of input and target, $P(X, Y)$.
@@ -72,9 +73,9 @@ Nonetheless, the platform might not support yet all possible combinations. The t
 #### Monitoring Metrics
 
 A Monitoring Metric is a generic quantity that can be computed on a Monitoring Target. They enable the monitoring of specific
-aspects of a target, which might help in identifying the root cause of a drift, as well as defining the corrective actions to be taken.
+aspects of an entity, which might help in identifying the root cause of a drift, as well as defining the corrective actions to be taken.
 
-The following table display the monitoring metrics supported, along with their monitoring target and the conditions
+The following table displays the monitoring metrics supported, along with their monitoring target and the conditions
 under which they are actually monitored. Notice that also this table is subject to changes, as new metrics will be added.
 
 | **Monitoring Metric** | Description                                              |              **Monitoring Target**               |             **Conditions**             |
@@ -83,7 +84,7 @@ under which they are actually monitored. Notice that also this table is subject
 |     TEXT_EMOTION      | The emotion of the text                                  |                INPUT, USER_INPUT                 |    When the data structure is text     |
 |    TEXT_SENTIMENT     | The sentiment of the text                                |                INPUT, USER_INPUT                 |    When the data structure is text     |
 |      TEXT_LENGTH      | The length of the text                                   | INPUT, USER_INPUT, RETRIEVED_CONTEXT, PREDICTION |    When the data structure is text     |
-|   MODEL_PERPLEXITY    | The uncertainty of the LLM                               |                    PREDICTION                    |       When the task type is RAG        |
+|   MODEL_PERPLEXITY    | A measure of how well the LLM predicts the next words    |                    PREDICTION                    |       When the task type is RAG        |
 |   IMAGE_BRIGHTNESS    | The brightness of the image                              |                      INPUT                       |    When the data structure is image    |
 |    IMAGE_CONTRAST     | The contrast of the image                                |                      INPUT                       |    When the data structure is image    |
 |      BBOXES_AREA      | The average area of the predicted bounding boxes         |                    PREDICTION                    | When the task type is Object Detection |