Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ModelPS: An Interactive and Collaborative Platform for Editing
  Pre-trained Models at Scale   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.

19
The categories of this paper are: eess
9
The categories of this paper are: nlin
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
14
The categories of this paper are: math-ph
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg