ModelPS: An Interactive and Collaborative Platform for Editing Pre-trained Models at Scale

TL;DR

ModelPS is a low-code, collaborative platform that simplifies editing and customizing deep neural network models at scale, reducing development time and enhancing team collaboration.

Contribution

The paper introduces ModelPS, a novel low-code platform with a visual interface and intelligent engine for collaborative DNN model editing during deployment.

Findings

Significantly reduces development overheads.

Enhances collaboration among developers.

Improves productivity in model customization.

Abstract

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.