Init Model page

Author: GiovanniGiacometti

md-docs/user_guide/model.md

@@ -1,7 +1,70 @@
 # Model
 
+In the ML Cube Platform, a Model is a representation of the actual machine learning model used for making predictions. The data used
+for its training usually represent the reference data distribution, while production data comprises the data on which the model 
+performs inference.
 
+A Model is uniquely associated with a [Task] and it can be created both through the WebApp and the Python SDK. Currently, we only support one model
+per Task.
 
+A Model is defined by a name and a version. The version is updated whenever the model is retrained, allowing to 
+track the latest version of the model and the data used for its training. When predictions are uploaded to the platform,
+the model version needs to be appropriately specified, following the guidelines in the [Data Schema] page, to ensure that the
+predictions are associated to the correct model version.
+
+!!! note
+    You don't need to upload the **real** model on the Platform. We only require its training data and predictions.
+    The entity you create on the Platform serves more as a placeholder for the model. For this reason,
+    the ML cube Platform is considered *model agnostic*.
+
+
+### RAG Model
+
+RAG Tasks represent an exception to the model framework presented before. In this type of Tasks, the model
+is a Large Language Model (LLM), that is used to generate responses to user queries. The model is not trained on a specific dataset
+but is rather a pre-trained model that is fine-tuned on the user's data, which means that the classic process of training and
+retraining does not apply. 
+
+To maintain a coherent Model definition across task types, the RAG model is also represented as a Model, 
+but an update of its version represents an update of the reference data distribution and not necessarily
+an update of the model itself. Moreover, most of the attributes which will be described in the following sections
+are not applicable, as they are related to the retraining module, which is not usable in RAG tasks.
+
+### Probabilistic output
+
+When creating a model, you can specify if you want to provide also the probabilistic output of the model along with the predictions. 
+The probabilistic output represents the probability or confidence score associated with the model's predictions. If provided, 
+the ML cube Platform will use this information to compute additional metrics and insights.
+
+It is optional and currently supported only for Classification and RAG tasks. If specified, the probabilistic output must be provided 
+as a new column in the predictions file, following the guidelines in the [Data Schema] page.
+
+### Metric
+
+A Model Metric represents the evaluation metric used to assess the performance of the model. 
+It can both represent a performance or an error. The chosen metric will be used in the various views of the platform to
+provide insights on the model's performance. The available options are:
+
+- `Accuracy`, for classification tasks
+- `RMSE`, for regression tasks
+- `R2`, for regression tasks
+- `Average Precision`, for Object Detection tasks
+
+RAG tasks have no metric, as in that case the model is an LLM for which classic definitions of metrics are not applicable.
+
+### Suggestion Type
+
+The Suggestion Type represents the type of suggestion that the ML cube Platform should provide when computing the 
+[retraining dataset](modules/retraining.md#retraining-dataset). The available options are:
+
+- `Sample Weights`: each sample uploaded in ML cube Platform is assigned a weight that can be used as sample weight in a weighted loss function.
+    The higher the weight, the greater the importance of the sample for the new retraining.
+- `Resampled Dataset`: a list of sample ids (using data schema column object with role ID) is provided indicating which data form the retraining dataset.
+    This format can be used when the training procedure does not support weighted loss or when a fixed size retraining dataset is preferred.
+    Note that samples ids can appear more than once: this happens when a sample is particularly important for the new retraining.
+
+[Task]: task.md
+[Data Schema]: data_schema.md
 
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md-docs/user_guide/monitoring/index.md

@@ -139,7 +139,7 @@ stateDiagram-v2
 ```
 
 
-Notice that a Drift OFF event can either bring the entity back to the `OK` status or to the `WARNING` status, 
+Notice that a Drift Off event can either bring the entity back to the `OK` status or to the `WARNING` status, 
 depending on the velocity of the change and the monitoring algorithm's sensitivity. The same applies
 to the Drift ON events, which can both happen when the entity is in the `WARNING` status or in the `OK` status.