From Acquiring to Suggesting DL Design Choices with Agility: A System Design

From Acquiring to Suggesting DL Design Choices with Agility: A System Design

연구 분야: Software Development

학회: International Conference on Research Challenges in Information Science

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.