발간년도 : [2023]
| 논문정보 |
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| 논문명(한글) |
[Vol.18, No.6] A Study on MLOps Development Environments for Effective Management of AI Workflow |
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| 논문투고자 |
Dong-Gil Kim, Tae-Yun Chung |
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| 논문내용 |
Most AI(Artificial Intelligence) models had difficulty entering the production environment beyond theresearch environment, and it is very important to adapt quickly to the environment and maintainperformance consistently depended on the situation. ML(Machine Learning) or DL(Deep Learning) thelifecycle consists of many complex component such as data ingest, data prep, model train, model tune,model monitoring, and does not end in service distribution, but can update the model throughcontinuous learned to reflect data change or new pattern, and as the trained process of the modelbecomes complicated and diverse, the need for MLOps(Machine Learning Operations) to manages andstandardize it is becomes important. However, because the approach is relatively early, there is a lack ofstructured literature or guidance, and expertise is required because it include a wide range of technologyfor commercialize such as container, kubernetes, data preprocess, and model distribution. Therefore, inthis study, the MLOps system for open-source computing resource integrated specialized in AI modeland high-performance computing research is described in depth based on multiple node, and thisprovided a system that could reproduce performance under the same condition in different environmentto automate time consumed and iterative model of workflow and track how hyper-parameter used indata affect model performance. |
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