This example uses uses a synthetically generated dataset of credit card score
observations, with 50,000 records in each dataset. They contain a set of
predictive attributes and a target value. The target value is the change
in an individual's credit score over a fixed period of time.

The data is stored as prepositioned Credit Score Data. Each data file is
placed on an independent Access Point, representing two credit bureaus.
The column named "target" is the "y" label, and the rest of the columns
get stored under the "x" data.

These datasets are used by organization 3 to perform a distributed and
private XGBoost model training. The resultant model resides on org 3's
Access Point and can be used by others with permission for a remote inference.
The model can also be download by org 3 for local inference.

Both federated and SMPC inference are showcased.
