본문 바로가기
  • Home

MLOps workflow language and platform for time series data anomaly detection

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2022, 27(11), pp.19-27
  • DOI : 10.9708/jksci.2022.27.11.019
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : September 22, 2022
  • Accepted : November 16, 2022
  • Published : November 30, 2022

Jung-Mo Sohn 1 Su-Min Kim 1

1이포즌

Accredited

ABSTRACT

In this study, we propose a language and platform to describe and manage the MLOps(Machine Learning Operations) workflow for time series data anomaly detection. Time series data is collected in many fields, such as IoT sensors, system performance indicators, and user access. In addition, it is used in many applications such as system monitoring and anomaly detection. In order to perform prediction and anomaly detection of time series data, the MLOps platform that can quickly and flexibly apply the analyzed model to the production environment is required. Thus, we developed Python-based AI/ML Modeling Language (AMML) to easily configure and execute MLOps workflows. Python is widely used in data analysis. The proposed MLOps platform can extract and preprocess time series data from various data sources (R-DB, NoSql DB, Log File, etc.) using AMML and predict it through a deep learning model. To verify the applicability of AMML, the workflow for generating a transformer oil temperature prediction deep learning model was configured with AMML and it was confirmed that the training was performed normally.

Citation status

* References for papers published after 2022 are currently being built.