Data observability for AI models
Here, we would like to mention some specific elements of data observability that focus on AI and ML methods. You can see the model as a particular case of a data source. The model is the output of a lineage. For this, you can follow the second notebook for this chapter, Orders_predict.
Let’s look at the different components of ML observability.
Model method
The model method is the name of the method that we will use to apply the transformation to create the model. It will be, for instance, the name of the scikit-learn classes you use or a more generic method name:
- Example of a library method:
Scikit::LinearRegression() - Example of a generic method: Random forest
The method is the ingredient you use to create the model data source. Inside the same application, you can try several methods and compare them. At this point, you must link the method to the right lineage. To do so, you must use the model training entity.
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