  Classical machine learning approaches are sensitive to non-stationarity.
Transfer learning can address non-stationarity by sharing knowledge from one
system to another, however, in areas like machine prognostics and defense, data
is fundamentally limited. Therefore, transfer learning algorithms have little,
if any, examples from which to learn. Herein, we suggest that these constraints
on algorithmic learning can be addressed by systems engineering. We formally
define transfer distance in general terms and demonstrate its use in
empirically quantifying the transferability of models. We consider the use of
transfer distance in the design of machine rebuild procedures to allow for
transferable prognostic models. We also consider the use of transfer distance
in predicting operational performance in computer vision. Practitioners can use
the presented methodology to design and operate systems with consideration for
the learning theoretic challenges faced by component learning systems.
