TY  - JOUR
T1  - A Study on the System for AI Service Production
AU  - Hong, Yong-Geun 
JO  - KIPS Transactions on Computer and Communication Systems
PY  - 2022
DA  - 2022/1/30
DO  - https://doi.org/10.3745/KTCCS.2022.11.10.323
KW  - Artificial Intelligence
KW  - Object Detection
KW  - AI Inference
KW  - AI Production
KW  - Edge Computing
AB  - As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AI 
technology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. In 
this paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and production 
of machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systems 
were built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of different 
AI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services through 
Tensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inference 
time than AI machine communication method, and in the case of object detection AI service, the number and complexity of objects
in the image are more affected than the file size of the image to be detected. In addition, it was confirmed that if the AI service is 
performed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service than 
if it is performed locally. Through the results of this study, it is expected that system design suitable for service goals, AI model development, 
and efficient AI service production will be possible.