Modeling new applications with deep learning (DL) algorithms requires substantial knowledge. Some systems aim to simplify design choices by providing support for specific pre-defined use cases, like blurred image backgrounds or text summaries, making it easier by limiting certain options. There is a gap in addressing diverse use cases and efficiently gathering knowledge output from the deep learning community to find and reuse models and datasets from various sources if they help solve a use case. In this experience study, we are interested in how to suggest and manage DL design choices stemming from artifacts published by the DL community to help non-expert users. We detail a system for this end using a business process (BP) model, discussing the requirements for software components implementing each BP model task. We also analyzed agility in recomposing pipelines using an in-house tool against open-sourced orchestration tools, implementing deep learning model adaptation components in one highly modular BP model task.