This change splits train.py into two files.

The new train.py is standalone, and has no references to AzureML. It defines two functions, split_data to split a dataframe into test/train data, and train_model which takes the test/train data and a parameter object and trains the model and returns related metrics. The script can be run locally, in which case it loads a dataset from a file.

The second file, train_aml.py contains reasonably general AzureML logic. It reads data from a dataset, then calls the split_data function from train.py. It loads input parameters from a config file and logs them, then calls train_model from train.py. It then uploads the model and logs any metrics returned by train_model.

The hope with these changes is to demonstrate a simple interface for integrating an existing ML script with MLOpsPython, as well as providing an example for how the core ML functionality can be invoked in multiple ways for development purposes.