1. A method for batched, supervised, in-situ machine learning classifier retraining for malware identification and model heterogeneity, the method comprising: a. producing a parent classifier model in one location and providing it to one or more in-situ retraining system or systems in a different location or locations; b. adjudicating the class determination of the parent classifier over the plurality of the samples evaluated by the in-situ retraining system or systems; c. determining a minimum number of adjudicated samples required to initiate the in-situ retraining process; d. blending a feature vector representation of the in-situ training and test sets with a feature vector representation of the parent training and test sets or subset thereof; e conducting machine learning over the blended training set; f evaluating the new and parent models using the blended test set and additional unlabeled samples; and g electing whether to replace the parent classifier with the retrained version.