With the evolution of the deep learning eco-system and the availability of open-source software packages such as TensorFlow and PyTorch, creating a quick proof-of-concept (PoC) has become a straightforward task. However, based on our experience, we contend that transitioning a project from PoC to Deployment is a difficult process in which the team must systematically consider a plethora of design and data- centric choices, which we refer to as R&D challenges. Some of the R&D challenges toward developing a successful deep learning-based system in the seismic processing domain are presented in this brief abstract. Recommendations on how to mitigate these challenges are discussed on a real-world example of automating salt interpretation.
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