Mungebits2 defines a way of thinking about data preparation that couples the definition of what happens in batch processing versus online prediction so that both can be described by the same codebase.
For example, consider the simple example of imputation. While the general concept of imputing a variable works on arbitrary codebases, a separate data transformation will have to be defined for each model that uses imputation in a production setting. This is because the imputed value depends inherently on the dataset. We must remember the mean of the data set encountered during training, and recall this value when performing replacement in a production setting.
Mungebits provide a sort of "train track switch" that allows one to write data preparation offline, but ensure it works online (on a stream of new data, such as one-row data.frames).
By reframing data preparation as the process of constructing a "munge procedure", a list of trained mungebits that can reproduce the same mathematical operation on a dataset in a production environment without additional code, the process of productionizing a machine learning model should become significantly simplified.