5. The method of claim 1 , further comprising: detecting an abrupt model drift in the machine learning model by computing an exponential moving average (EMA) quality using a window of a most recent training data, and comparing the EMA quality with an overall quality measure of the machine learning model.
1. A computer-implemented method of quality-directed adaptive analytic retraining, comprising: 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, wherein determining whether to retrain the machine learning model at least based on the normalized quality measure, comprises: determining whether the quality measure is below a quality threshold; and determining whether a number of available data items comprising at least the training example data meet a specified number of inertia window data items; wherein responsive to determining that the quality measure is below the quality threshold and the number of available data items comprising at least the training example data meets the specified number of inertia window data items, the machine learning model is retrained.