MLOps workflow language and platform for time series data anomaly detection (시계열 데이터 이상탐지를 위한 AI MLOps Workflow 언어와 플랫폼)

Jung-Mo Sohn (손정모); Su-Min Kim (김수민)

doi:10.9708/jksci.2022.27.11.019

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.

KEYWORDS

AI, MLOps, Workflow, Time series data, Anomaly detection

Citation status

* References for papers published after 2023 are currently being built.

[journal] 이요섭 / 2017 / A Comparison and Analysis of Deep Learning Framework / 한국전자통신학회 논문지 / 한국전자통신학회 12(1) : 115~122

[journal] 배성완 / 2018 / Predicting the Real Estate Price Index Using Machine Learning Methods and Time Series Analysis Model / 주택연구 / 한국주택학회 26(1) : 107~133

[other] Samuel Ackerman / 2021 / Automatically detecting data drift in machine learning classifiers

[confproc] Sasu Makinen / 2021 / Who needs MLOps: What data scientists seek to accomplish and how can MLOps help? / 2021 IEEE/ACM 1st Workshop on AI Engineering-Software Engineering for AI (WAIN) / IEEE : 109~112

[confproc] Sima Siami-Namini / 2018 / A Comparison of ARIMA and LSTM in Forecasting Time Series / 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) : 1394~1401

[confproc] Georgios Symeonidis / 2022 / MLOps - Definitions, Tools and Challenges / 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC) : 0453~0460

[other] Dominik Kreuzberger / 2022 / Machine Learning Operations (MLOps): Overview, Definition, and Architecture

[web] Google / MLOps: Continuous delivery and automation pipelines in machine learning / https://cloud.google.com/architecture/mlopscontinuous-delivery-and-automation-pipelines-in-machine-learning?hl=en

[web] Microsoft / Machine Learning operations maturity model / https://docs.microsoft.com/en-us/azure/architecture/example-scenario/mlops/mlops-maturity-model

[web] MLflow Project / MLflow - A platform for the machine learning lifecycle / https://mlflow.org

[web] The Kubeflow Authors / Kubeflow / https://www.kubeflow.org

[other] Gustavo Correa Publio / 2018 / ML-schema: exposing the semantics of machine learning with schemas and ontologies

[journal] 문종혁 / 2021 / Deep Learning Description Language for Referring to Analysis Model Based on Trusted Deep Learning / 정보처리학회 논문지 / 한국정보처리학회 10(4) : 133~142

[journal] Sepp Hochreiter / 1997 / Long Short-Term Memory / Neural Computation 9(8) : 1735~1780

[confproc] Sima Siami-Namini / 2018 / A Comparison of ARIMA and LSTM in Forecasting Time Series / 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA) : 1394~1401

[web] YAML Language Development Team / YAML Ain’t Markup Language (YAML™) version 1.2 / https://yaml.org/spec/1.2.2/

[web] Haoyi Zhou / Electricity Transformer Dataset (ETDataset) / https://github.com/zhouhaoyi/ETDataset

[confproc] Haoyi Zhou / 2021 / Informer: Beyond Efficient Transformer for Long SequenceTime-Series Forecasting / Proceedings of the AAAI Conference on Artificial Intelligence 35(12) : 11106~11115

[web] Google / Tabular Workflows on Vertex AI / https://cloud.google.com/vertex-ai/docs/tabular-data/tabular-workflows/overview?hl=en

[web] Microsoft / Create and run machine learning pipelines using components with the Azure Machine Learning studio(Preview) / https://docs.microsoft.com/en-us/azure/machine-learning/how-tocreate-component-pipelines-ui

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Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

MLOps workflow language and platform for time series data anomaly detection

ABSTRACT

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Citation status

* References for papers published after 2023 are currently being built.

Journal of The Korea Society of Computer and Information 2023 KCI Impact Factor : 0.65

MLOps workflow language and platform for time series data anomaly detection

ABSTRACT

KEYWORDS

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* References for papers published after 2023 are currently being built.