인공지능 서비스 운영을 위한 시스템 측면에서의 연구
A Study on the System for AI Service Production
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초록
AI 기술을 활용한 다양한 서비스가 개발되면서, AI 서비스 운영에 많은 관심이 집중되고 있다. 최근에는 AI 기술도 하나의 ICT 서비스를보고, 범용적인 AI 서비스 운영을 위한 연구가 많이 진행되고 있다. 본 논문에서는 일반적인 기계학습 개발 절차의 마지막 단계인 기계학습모델 배포 및 운영에 초점을 두고 AI 서비스 운영을 위한 시스템 측면에서의 연구 결과를 기술하였다. 3대의 서로 다른 Ubuntu 시스템을 구축하고,이 시스템상에서 서로 다른 AI 모델(RFCN, SSD-Mobilenet)과 서로 다른 통신 방식(gRPC, REST)의 조합으로 2017 validation COCO dataset의데이터를 이용하여 객체 검출 서비스를 Tensorflow serving을 통하여 AI 서비스를 요청하는 부분과 AI 서비스를 수행하는 부분으로 나누어실험하였다. 다양한 실험을 통하여 AI 모델의 종류가 AI 머신의 통신 방식보다 AI 서비스 추론 시간에 더 큰 영향을 미치고, 객체 검출 AI서비스의 경우 검출하려는 이미지의 파일 크기보다는 이미지 내의 객체 개수와 복잡도에 따라 AI 서비스 추론 시간이 더 큰 영향을 받는다는것을 알 수 있었다. 그리고, AI 서비스를 로컬이 아닌 원격에서 수행하면 성능이 좋은 머신이라고 하더라도 로컬에서 수행하는 경우보다 AI서비스 추론 시간이 더 걸린다는 것을 확인할 수 있었다. 본 연구 결과를 통하여 서비스 목표에 적합한 시스템 설계와 AI 모델 개발 및 효율적인AI 서비스 운영이 가능해질 것으로 본다.
Abstract
As various services using AI technology are being developed, much attention is being paid to AI service production. Recently, AItechnology is acknowledged as one of ICT services, a lot of research is being conducted for general-purpose AI service production. Inthis paper, I describe the research results in terms of systems for AI service production, focusing on the distribution and productionof machine learning models, which are the final steps of general machine learning development procedures. Three different Ubuntu systemswere built, and experiments were conducted on the system, using data from 2017 validation COCO dataset in combination of differentAI models (RFCN, SSD-Mobilenet) and different communication methods (gRPC, REST) to request and perform AI services throughTensorflow serving. Through various experiments, it was found that the type of AI model has a greater influence on AI service inferencetime than AI machine communication method, and in the case of object detection AI service, the number and complexity of objectsin 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 isperformed remotely rather than locally, even if it is a machine with good performance, it takes more time to infer the AI service thanif 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.
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