Merge pull request #16 from ml-cube/dev-monitoring

Add monitoring section

Author: alelavml3

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

@@ -30,4 +30,12 @@
   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;
+}
+
+.mermaid {
+    text-align: center;
+ }

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

@@ -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/imgs/monitoring/drift-explainability/score.svg

@@ -53,7 +53,7 @@ A **Task** is specified by several attributes, the most important are:
 
 - `type`: regression, classification, object detection ...
 - `data structure`: tabular data, image data, ...
-- `optional target`: if the target is not always available. This happen when input samples are labeled and the most part of production data do not have a label
+- `optional target`: if the target is not always available. This happens when input samples are labeled and the most part of production data do not have a label
 - `data schema`: specifies the inputs and the target of the task, see [Data Schema](data_schema.md) section for more details
 - `cost info`: information about the economic costs of the error on the target
 
@@ -110,7 +110,7 @@ Now that you have clear the basic concepts we invite you to explore the other ML
 
     Discover how to setup automation rules to increase your reactivity.
 
-    [:octicons-arrow-right-24: More info](detection_event_rules.md)
+    [:octicons-arrow-right-24: More info](monitoring/detection_event_rules.md)
 
 -   :material-lock:{ .lg .middle } **Roles and access**
 

md-docs/imgs/monitoring/overview.svg

@@ -1 +1,20 @@
-# Model
+# Model
+
+
+
+
+[//]: # ()
+[//]: # ()
+[//]: # (What is additional probabilistic output?)
+
+[//]: # ()
+[//]: # (What is metric?)
+
+[//]: # ()
+[//]: # (What is suggestion type?)
+
+[//]: # ()
+[//]: # (What is retraining cost?)
+
+[//]: # ()
+[//]: # (What is retraining trigger?)