  Researchers have been highly active to investigate the classical machine
learning workflow and integrate best practices from the software engineering
lifecycle. However, deep learning exhibits deviations that are not yet covered
in this conceptual development process. This includes the requirement of
dedicated hardware, dispensable feature engineering, extensive hyperparameter
optimization, large-scale data management, and model compression to reduce size
and inference latency. Individual problems of deep learning are under thorough
examination, and numerous concepts and implementations have gained traction.
Unfortunately, the complete end-to-end development process still remains
unspecified. In this paper, we define a detailed deep learning workflow that
incorporates the aforementioned characteristics on the baseline of the
classical machine learning workflow. We further transferred the conceptual idea
into practice by building a prototypic deep learning system using some of the
latest technologies on the market. To examine the feasibility of the workflow,
two use cases are applied to the prototype.
