[julienkervizic]

Yes it covers a bit more than what people tend to see in Machine Learning which is the model training part. From the code base it looks to cover:

1) feature preparation and model training as part of notebooks

2) Creation of a Flask API to interact with the trained models. This includes feature enrichment based on APIs input, so as to match the expected inputs by the model

3) Creation of an UI to interact with the API/Model

4) Setup of NGinx and docker to surface that application

Each part is covered in a minimal manner compared to most enterprise data products, and only cover 1 of the approach for end to end ML, but I think it does the job well to demonstrate the scope of work that is needed to put ML data products into production, and could be used as good introduction.