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Researchers have been highly active to investigate the classical machinelearning workflow and integrate best practices from the software engineeringlifecycle. However, deep learning exhibits deviations that are not yet coveredin this conceptual development process. This includes the requirement ofdedicated hardware, dispensable feature engineering, extensive hyperparameteroptimization, large-scale data management, and model compression to reduce sizeand inference latency. Individual problems of deep learning are under thoroughexamination, and numerous concepts and implementations have gained traction.Unfortunately, the complete end-to-end development process still remainsunspecified. In this paper, we define a detailed deep learning workflow thatincorporates the aforementioned characteristics on the baseline of theclassical machine learning workflow. We further transferred the conceptual ideainto practice by building a prototypic deep learning system using some of thelatest technologies on the market. To examine the feasibility of the workflow,two use cases are applied to the prototype.

Continuous Deep Learning: A Workflow to Bring Models into Production