Abstract: Methods and apparati for continuous growth, re-use, and application of automated labelers 4, 7 for machine learning algorithms into ensembles 10. A method embodiment of the present invention comprises an iterative cycle (steps 11 through 15) in which data 2 is collected, indexed, and then used to create labelers 4 to generate training data for supervised and semi-supervised machine learning algorithms. A new set of unlabeled training data 5 is then similarly indexed and combined with the most similar, relevant, or useful previous labelers 4 by means of index 6, 3 comparisons in order to create an optimized ensemble 10 of labelers 4, 7, thus maximizing the training value of the labels generated from the labelers 4, 7.