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

Rakshith Subramanya; Seppo Sierla; Valeriy Vyatkin

doi:10.3390/app12199851

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

Rakshith Subramanya^*
, Seppo Sierla
, Valeriy Vyatkin

^*Tämän työn vastaava kirjoittaja

Luleå University of Technology

Tutkimustuotos: Lehtiartikkeli › Article › Scientific › vertaisarvioitu

72 Sitaatiot (Scopus)

544 Lataukset (Pure)

Abstrakti

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.

Alkuperäiskieli	Englanti
Artikkeli	9851
Sivumäärä	31
Julkaisu	Applied Sciences
Vuosikerta	12
Numero	19
DOI - pysyväislinkit	https://doi.org/10.3390/app12199851
Tila	Julkaistu - lokak. 2022
OKM-julkaisutyyppi	A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Rahoitus

This work was supported in part by Business Finland under grant 7439/31/2018.

Pääsy asiakirjaan

10.3390/app12199851Lisenssi: CC BY

applsci-12-09851Final published version, 7,55 MBLisenssi: CC BY

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Sierla, S. (Vastuullinen johtaja), Rantala, K. (Projektin jäsen), Yli-Ojanperä, M. (Projektin jäsen), Vyatkin, V. (Projektin jäsen), Subramanya, R. (Projektin jäsen), Aaltonen, H. (Projektin jäsen), Karhula, N. (Projektin jäsen) & Hölttä, T. (Projektin jäsen)
01/04/2019 → 31/03/2022
Projekti: Business Finland: Other research funding

Science-IT
Hakala, M. (Manager)
Perustieteiden korkeakoulu
Laitteistot/tilat: Facility

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@article{8f8c0b8cefed4f5894bf88a58e1002e0,

title = "From DevOps to MLOps: Overview and Application to Electricity Market Forecasting",

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.",

keywords = "continuous software engineering, DevOps, electricity market, Machine Learning, MLOps, time-series analysis",

author = "Rakshith Subramanya and Seppo Sierla and Valeriy Vyatkin",

note = "Funding Information: This work was supported in part by Business Finland under grant 7439/31/2018. Publisher Copyright: {\textcopyright} 2022 by the authors.",

year = "2022",

month = oct,

doi = "10.3390/app12199851",

language = "English",

volume = "12",

journal = "Applied Sciences",

issn = "2076-3417",

publisher = "MDPI AG",

number = "19",

}

TY - JOUR

T1 - From DevOps to MLOps

T2 - Overview and Application to Electricity Market Forecasting

AU - Subramanya, Rakshith

AU - Sierla, Seppo

AU - Vyatkin, Valeriy

PY - 2022/10

Y1 - 2022/10

N2 - 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.

AB - 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.

KW - continuous software engineering

KW - DevOps

KW - electricity market

KW - Machine Learning

KW - MLOps

KW - time-series analysis

UR - https://www.scopus.com/pages/publications/85139914234

U2 - 10.3390/app12199851

DO - 10.3390/app12199851

M3 - Article

AN - SCOPUS:85139914234

SN - 2076-3417

VL - 12

JO - Applied Sciences

JF - Applied Sciences

IS - 19

M1 - 9851

ER -

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

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