A quality-directed adaptive analytic retraining is provided. Training example data with which to retrain a machine learning model that has been previously trained is received. The training example data is stored in a memory. The machine learning model is evaluated at least by running the machine learning model with the training example data. A normalized quality measure may be determined based on the evaluating. Whether to retrain the machine learning model is determined at least based on the normalized quality measure. Responsive to determining that the machine learning model is to be retrained, the machine learning model is retrained.
Please help me write a proper abstract based on the patent claims. CLAIM: 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. 2. The method of claim 1, 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, wherein responsive to determining that the quality measure is below the quality threshold, the machine learning model is retrained. 3. The method of claim 1, 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. 4. The method of claim 1, wherein the evaluating comprises performing a prediction by running the machine learning model with the training example data, and updating one or more of a loss function for supervised learning or a cost function for unsupervised learning. 5. The method of claim 1, wherein the method further comprises: responsive to determining that the machine learning model is to be retrained, selecting a retraining data set. 6. The method of claim 5, wherein the selecting a retraining data set comprises one or more of: selecting all available training data comprising initial training data used to train the machine learning model initially if available and the training example data; selecting training data available since a last retraining of the machine learning model; or selecting a sliding window amount of data of a most recent training data, wherein the machine learning model is retrained with the selected retraining data set. 7. 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. 8. The method of claim 7, further comprising: determining whether a threshold amount of training data is available since the abrupt model drift; and responsive to determining that the threshold amount of training data is available, triggering a start of retraining of the machine learning model. 9. A computer readable storage medium storing a program of instructions executable by a machine to perform a method of quality-directed adaptive analytic retraining, the method 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 not determined that the machine learning model should be retrained. 10. The computer readable storage medium of claim 9, 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, wherein responsive to determining that the quality measure is below the quality threshold, the machine learning model is retrained. 11. The computer readable storage medium of claim 9, 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 for retraining 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. 12. The computer readable storage medium of claim 9, wherein the evaluating comprises performing a prediction by running the machine learning model with the training example data, and updating one or more of a loss function for supervised learning or a cost function for unsupervised learning. 13. The computer readable storage medium of claim 9, wherein the method further comprises: responsive to determining that the machine learning model is to be retrained, selecting a retraining data set. 14. The computer readable storage medium of claim 13, wherein the selecting a retraining data set comprises one or more of: selecting all available training data comprising initial training data used to train the machine learning model initially if available and the training example data; selecting training data available since a last retraining of the machine learning model; or selecting a sliding window amount of data of a most recent training data, wherein the machine learning model is retrained with the selected retraining data set. 15. The computer readable storage medium of claim 9, 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. 16. The computer readable storage medium of claim 15, further comprising: determining whether a threshold amount of training data is available since the abrupt model drift; and responsive to determining that the threshold amount of training data is available, triggering a start of retraining of the machine learning model. 17. A system for quality-directed adaptive analytic retraining, comprising: one or more processors operable to receive training example data with which to retrain a machine learning model that has been previously trained; and a memory device operable to store the training example data; one or more of the processors further operable to evaluate the machine learning model at least by running the machine learning model on a processor with the training example data, one or more of the processors further operable to determine a normalized quality measure based on the evaluating, one or more of the processors further operable to determine 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, one or more of the processors further operable to retrain the machine learning model, wherein responsive to determining that the machine learning model does not need to be retrained, the training example data is accumulated without retraining the machine learning model. 18. The system of claim 17, wherein one or more of the processors determines whether to retrain the machine learning model at least based on the normalized quality measure, by one or more of: determining whether the quality measure is below a quality threshold, wherein responsive to determining that the quality measure is below the quality threshold, the machine learning model is retrained; or 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 meet the specified number of inertia window data items, the machine learning model is retrained. 19. The system of claim 17, wherein one or more of the processors is further operable to select a retraining data set by one or more of: selecting all available training data comprising initial training data used to train the machine learning model initially if available and the training example data; selecting training data available since a last retraining of the machine learning model; or selecting a sliding window amount of data of a most recent training data, wherein the machine learning model is retrained with the selected retraining data set. 20. The system of claim 1, wherein one or more of the processors is further operable to detect 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, and one or more of the processors further operable to determine whether a threshold amount of training data is available since the abrupt model drift, responsive to determining that the threshold amount of training data is available, one or more of the processors further operable to trigger a start of retraining of the machine learning model.