ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale
This addresses the problem for software developers by providing a user-friendly tool for collaborative model editing, though it is incremental as it builds on existing low-code and model editing concepts.
The paper tackles the challenge of editing pre-trained deep neural network models during deployment by introducing ModelPS, a low-code platform that enables collaborative editing and intelligent serving, which case studies show can significantly reduce development and communication overheads with improved productivity.
AI engineering has emerged as a crucial discipline to democratize deep neural network (DNN) models among software developers with a diverse background. In particular, altering these DNN models in the deployment stage posits a tremendous challenge. In this research, we propose and develop a low-code solution, ModelPS (an acronym for "Model Photoshop"), to enable and empower collaborative DNN model editing and intelligent model serving. The ModelPS solution embodies two transformative features: 1) a user-friendly web interface for a developer team to share and edit DNN models pictorially, in a low-code fashion, and 2) a model genie engine in the backend to aid developers in customizing model editing configurations for given deployment requirements or constraints. Our case studies with a wide range of deep learning (DL) models show that the system can tremendously reduce both development and communication overheads with improved productivity.