Deployment of a model is the process by which you put your models into a real-world production setting. This can depend on many factors, such as the environment in which it was developed, the algorithm that was chosen, assumptions concerning the data that was made when the model was developed, and of course, the level of the developer. Often a model is unable to scale up to the demands of a production environment and knowing your possible production environment in advance will dictate what problems or techniques are feasible.
Model scoring makes the model actionable. If you develop a model and you are unable to apply the results to new data, then you will be unable to do any prediction on an ongoing basis. New model scoring often involves outputing the development model outputs to a real-time scoring engine. That engine is often Java or C++. How that is performed varies vastly depending upon the modeling technique. Sometimes the scoring is performed separately...