Final Thoughts on Delivering a Predictive Model to the Client

We have now completed the modeling activities and also created a financial analysis to indicate to the client how they can use the model. While we have completed the essential intellectual contributions that are the data scientist's responsibility, it is necessary to agree with the client on the form in which all these contributions will be delivered.

A key contribution is the predictive capability embodied in the trained model. Assuming the client can work with the trained model object we created with XGBoost, this model could be saved to disk as we've done and sent to the client. Then, the client would be able to use it within their workflow. This pathway to model delivery may require the data scientist to work with engineers in the client's organization, to deploy the model within the client's infrastructure.

Alternatively, it may be necessary to express the model as a mathematical equation...