ArtifactOps and ArtifactDL: a methodology and a language for conceptualizing and operationalising different types of pipelines

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dc.contributor.authorMiñón Jiménez, Raúl
dc.contributor.authorDíaz de Arcaya Serrano, Josu
dc.contributor.authorTorre Bastida, Ana Isabel
dc.contributor.authorLópez de Armentia Mendizabal, Juan
dc.contributor.authorZárate Martínez, Gorka
dc.contributor.authorBonilla, Lander
dc.contributor.authorGarcia Perez, Asier
dc.contributor.authorAguirre Usandizaga, Jon
dc.date.accessioned2026-04-13T07:20:06Z
dc.date.available2026-04-13T07:20:06Z
dc.date.issued2025-08-07
dc.date.updated2026-04-13T07:20:06Z
dc.description.abstractMachine learning is already integrated in diverse domains enhancing their performance and decision support. For laboratories, this approach is normally sufficient. However, in real environments, these models can not be generally deployed isolated since they require additional steps to satisfy an objective. These steps can range from different data transformations to the inclusion of extra machine learning models which compose an analytic pipeline. Moreover, the majority of software solutions wrap a model into an API and, rarely, focus on the whole pipeline. These are unresolved topics in the well-known MLOps methodology, specifically in packaging and service phases. In addition, these concerns can also be extrapolated to other paradigms like DevOps or DataOps. In the context of the Pliades European project, this paper approaches the conceptualization of diverse types of pipelines from different perspectives and for different contexts, instead of simplifying the deployment and serving to an API. Thus, ArtifactOps methodology is proposed aimed at unifying XXOps paradigms which share the majority of stages. Finally, ArtifactDL pipeline definition language is proposed to describe the key aspects identified when designing different pipelines types and to support the proposed ArtifactOps methodology. Moreover, the research presents two real scenarios to better illustrate both ArtifactOps methodology and ArtifactDL pipeline definition language and it is defined an expert evaluation conducted to validate the approach.en
dc.description.sponsorshipEU Horizon Europe funded project PLIADES under the Grant Agreement No: 101135988en
dc.identifier.citationMiñón, R., Diaz-de-Arcaya, J., Torre-Bastida, A. I., López-de-Armentia, J., Zarate, G., Bonilla, L., Garcia-Perez, A., & Aguirre-Usandizaga, J. (2025). ArtifactOps and ArtifactDL: a methodology and a language for conceptualizing and operationalising different types of pipelines. Journal of Cloud Computing, 14(1). https://doi.org/10.1186/S13677-025-00761-W
dc.identifier.doi10.1186/S13677-025-00761-W
dc.identifier.eissn2192-113X
dc.identifier.urihttps://hdl.handle.net/20.500.14454/5600
dc.language.isoeng
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.rights© The Author(s) 2025
dc.subject.otherDataOps
dc.subject.otherDevOps
dc.subject.otherMLOps
dc.subject.otherPipelines
dc.subject.otherUnified methodology
dc.titleArtifactOps and ArtifactDL: a methodology and a language for conceptualizing and operationalising different types of pipelinesen
dc.typejournal article
dcterms.accessRightsopen access
oaire.citation.issue1
oaire.citation.titleJournal of Cloud Computing
oaire.citation.volume14
oaire.licenseConditionhttps://creativecommons.org/licenses/by/4.0/
oaire.versionVoR
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