1. A method of quality-directed adaptive analytic retraining, comprising executing on a processor: receiving training example data with which to retrain a machine learning model that has been previously trained; storing the training example data in a memory; evaluating the machine learning model at least by running the machine learning model with the training example data; determining a normalized quality measure based on the evaluating; determining whether to retrain the machine learning model at least based on the normalized quality measure; and responsive to determining that the machine learning model is to be retrained, retraining the machine learning model, wherein the machine learning model is not retrained if it is determined that the machine learning model is not to be retrained.