Most of the content available on ML projects, either through books, blogs, or tutorials, explains the mechanics of machine learning in such a way that the dataset available is split into training, validation, and test datasets. Models are built using training datasets, and model improvements through hyperparameter tuning are done iteratively through validation data. Once a model is built and improved upon to a point that is acceptable, it is tested for goodness with unseen test data and the results of testing are reported out. Most of the public content available, ends at this point.
In reality, the ML projects in a business situation go beyond this step. We may observe that if one stops at testing and reporting a built model performance, there is no real use of the model in terms of predicting about data that is coming up in future. We also need to realize that the idea of building a model is to be able to deploy the model in production and have the predictions based...