  Machine learning has proven to be an indispensable tool in the selection of
interesting events in high energy physics. Such technologies will become
increasingly important as detector upgrades are introduced and data rates
increase by orders of magnitude. We propose a toolkit to enable the creation of
a drone classifier from any machine learning classifier, such that different
classifiers may be standardised into a single form and executed in parallel. We
demonstrate the capability of the drone neural network to learn the required
properties of the input neural network without the use of any labels from the
training data, only using appropriate questioning of the input neural network.
