Unverified Commit 8a39f28a authored by dianajlailaty's avatar dianajlailaty Committed by GitHub
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Adding the documentation about the MaaS_ML data analytics dashboard (#769)

parent 8d40b2da
......@@ -798,9 +798,48 @@ You can also track the traceability information of the token during several date
It is possible to visualize the model predictions by clicking on the first link in the top of the page.
This link will take you to a *Predictions Preview* page that lists the set of predictions corresponding to the input dataset.
==== MaaS_ML Data Analytics
If you click on the link "Click here for MaaS_ML data analytics" provided on the top of the Audit and
Traceability page, you will be redirected to MaaS_ML Data Analytics page which contains three tabs:
** Dataset Analytics
** Data Drift Analytics
** Predictions Preview
===== Dataset Analytics
As MaaS_ML supports versioning, you are able to deploy multiple model versions for the same model type.
When deploying different model versions, you have the possibility to associate each version
with a subset of the data used to train the model i.e. the baseline data. The main job of the baseline data is to help in
detecting drifts in the future input datasets. Data drift detection is detailed in <<_data_drift_detection_ddd>> subsection.
Using the several baseline datasets, optionally, deployed with the different model versions, you are able to compare the changes
occurring from one model version to another, specifically regarding the datasets used to train them.
As shown in the figure below, using the three dropdowns on the top of this tab page, you can choose the model name, the feature (or column)
name you would like to monitor and the metric which is based on some data statistical functions (Mean, Minimum, Maximum, Variance, Standard Deviation).
By choosing these three values, the first graph
will show the evolution of the values (according to the chosen statistical function) of the chosen feature relative to
the different model versions. You also have the possibility to monitor multiple features at the same time by choosing multiple feature
names in the second dropdown. You can add
or remove any of the displayed graphical lines using the features dropdown. Details about the obtained values are displayed by hovering over
the markers on each graphical line.
If you click on one of these markers, a histogram will appear in the second graph of this tab page. The displayed
histogram shows a comparison of the probability density distributions of the data values of the selected feature
among all the deployed model version. By clicking on the content of the legend, you can include or exclude from the comparison any of the model
===== Data Drift Analytics
Coming soon!
===== Predictions Preview
When the user calls a deployed model of a specific version to obtain some predictions, he can choose to save the resulting predictions.
The saved predictions can be previewed in the Predictions Preview tab page. As shown in the figure below, you can choose the model name and the model
version using the dropdowns in the top of the page. According to your choices, the predictions dataframe will be previewed. The figure below shows an
example of the previewed predictions.
=== MaaS_ML Via Swagger UI

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