1. A computer implemented 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, by a user, one or more class determinations of the parent classifier model over a plurality of samples evaluated by the one or more in-situ retraining system or systems; c. determining a minimum number of adjudicated samples required to initiate an in-situ retraining process; d. blending a feature vector representation of in-situ training and test sets with a feature vector representation of parent training and test sets or subset thereof to form a blended training set: e. conducting machine learning over the blended training set to create a new classifier model: f. evaluating the new classifier model and the parent classifier model using the blended training set and unlabeled samples not included in the blended training set; and g. electing whether to replace the parent classifier model with the new classifier model.