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From DevOps to MLOps: Overview and Application to Electricity Market Forecasting

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

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en

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31

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Applied Sciences, Volume 12, issue 19

Abstract

In the Software Development Life Cycle (SDLC), Development and Operations (DevOps) has been proven to deliver reliable, scalable software within a shorter time. Due to the explosion of Machine Learning (ML) applications, the term Machine Learning Operations (MLOps) has gained significant interest among ML practitioners. This paper explains the DevOps and MLOps processes relevant to the implementation of MLOps. The contribution of this paper towards the MLOps framework is threefold: First, we review the state of the art in MLOps by analyzing the related work in MLOps. Second, we present an overview of the leading DevOps principles relevant to MLOps. Third, we derive an MLOps framework from the MLOps theory and apply it to a time-series forecasting application in the hourly day-ahead electricity market. The paper concludes with how MLOps could be generalized and applied to two more use cases with minor changes.

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Funding Information: This work was supported in part by Business Finland under grant 7439/31/2018. Publisher Copyright: © 2022 by the authors.

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Subramanya, R, Sierla, S & Vyatkin, V 2022, 'From DevOps to MLOps : Overview and Application to Electricity Market Forecasting', Applied Sciences, vol. 12, no. 19, 9851. https://doi.org/10.3390/app12199851

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