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. The code has been released as an open-source package at GitHub.
翻译:AI工程已成为使具有不同背景的软件开发者实现深神经网络模型民主化的关键学科。 特别是,在部署阶段改变这些DNN模型带来了巨大的挑战。 在这一研究中,我们提出并开发了一种低代码解决方案,即模型PS(“ Model Photoshop ” 缩略语),以扶持和增强合作DNN模型编辑和智能模型。模型PS解决方案包含两个变革性特征:1) 一个用户友好的网络界面,供开发者团队以低代码方式分享和编辑DNN模型模型,2) 后端的一个模型精灵引擎,用于帮助开发者根据特定部署要求或限制定制模式编辑配置模式配置。我们具有广泛深度学习模型的案例研究显示,该系统可以极大地减少开发和通信管理,提高生产率。代码已作为GitHub的公开源软件包发布。