  We propose methods to infer properties of the execution environment of
machine learning pipelines by tracing characteristic numerical deviations in
observable outputs. Results from a series of proof-of-concept experiments
obtained on local and cloud-hosted machines give rise to possible forensic
applications, such as the identification of the hardware platform used to
produce deep neural network predictions. Finally, we introduce boundary samples
that amplify the numerical deviations in order to distinguish machines by their
predicted label only.
