Data labeling for deep-learning models

A computer program product for managing deep-learning, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a device to cause the device to:

• deploy a first and a second scoring endpoint with payload logging for a deep-learning model;
• receive, at the second scoring endpoint, native data and a user-generated score for the native data;
• pre-process, at the second scoring endpoint, the native data into readable data for the deep-learning model;
• output, from the second scoring endpoint to the first scoring endpoint, the user-generated score for the native data and the readable data, wherein the first scoring endpoint is associated directly with the deep-learning model;
• output, from the second scoring endpoint to a payload store, a raw payload, wherein the raw payload includes the native data;
• process, at the first scoring endpoint and using the deep-learning model, the readable data and the user-generated score to output a transformed payload and a prediction, respectively, to the payload store;
• match, at the payload store, the raw payload with the transformed payload and the prediction to produce a comprehensive data set;
• evaluate the comprehensive data set to describe a set of transformation parameters;
• and
• retrain the deep-learning model to account for the set of transformation parameters.