Docker-izing the model training and export

We are going to utilize essentially the same code for model training as that from Chapter 4, Regression. However, we are going to make a few tweaks to the code to make it more user-friendly and able to interface with other portions of our workflow. We would not consider these complications to the actual modeling code. Rather, these are things that you would probably do to any application that you are getting ready to utilize more generally.

First, we are going to add some command line flags to our application that will allow us to specify the input directory where our training dataset will be located, and an output directory where we are going to export a persisted representation of our model. You ...

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