MLPacker: A Unified Software Tool for Packaging and Deploying Atomic and Distributed Analytic Pipelines

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dc.contributor.authorMinon, Raul
dc.contributor.authorDiaz-De-Arcaya, Josu
dc.contributor.authorTorre-Bastida, Ana I.
dc.contributor.authorZarate, Gorka
dc.contributor.authorMoreno-Fernandez-De-Leceta, Aitor
dc.contributor.editorSolic, Petar
dc.contributor.editorNizetic, Sandro
dc.contributor.editorRodrigues, Joel J. P. C.
dc.contributor.editorRodrigues, Joel J.P.C.
dc.contributor.editorGonzalez-de-Artaza, Diego Lopez-de-Ipina
dc.contributor.editorPerkovic, Toni
dc.contributor.editorCatarinucci, Luca
dc.contributor.editorPatrono, Luigi
dc.contributor.institutionHPA
dc.date.accessioned2024-07-24T11:54:13Z
dc.date.available2024-07-24T11:54:13Z
dc.date.issued2022
dc.descriptionPublisher Copyright: © 2022 University of Split, FESB.
dc.description.abstractIn the last years, MLOps (Machine Learning Operations) paradigm is attracting the attention from the community, extrapolating the DevOps (Development and Operations) paradigm to the artificial intelligence (AI) development life-cycle. In this area, some challenges must be addressed to successfully deliver solutions since there are specific nuances when dealing with AI operationalization such as the model packaging or monitoring. Fortunately, interesting and helpful approaches, both from the research community and industry have emerged. However, further research is still necessary to fulfil key gaps. This paper presents a tool, MLPacker, for addressing some of them. Concretely, this tool provides mechanisms to package and deploy analytic pipelines both in REST APIs and in streaming mode. In addition, the analytic pipelines can be deployed atomically (i.e., the whole pipeline in the same machine) or in a distributed fashion (i.e., deploying each stage of the pipeline in distinct machines). In this way, users can take advantage from the cloud continuum paradigm considering edge-fog-cloud computing layers. Finally, the tool is decoupled from the training stage to avoid data scientists the integration of blocks of code in their experiments for the operationalization. Besides the package mode (REST API or streaming), the tool can be configured to perform the deployments in local or in remote machines and by using or not containers. For this aim, this paper describes the gaps this tool addresses, the detailed components and flows supported, as well as an scenario with three different case studies to better explain the research conducted.en
dc.description.sponsorshipThe work presented in this paper has been partially supported by the SPRI Basque Government through their ELKA-RTEK program (HODEI-X project, ref.KK-2021/00049).
dc.description.statusPeer reviewed
dc.identifier.citationMinon , R , Diaz-De-Arcaya , J , Torre-Bastida , A I , Zarate , G & Moreno-Fernandez-De-Leceta , A 2022 , MLPacker : A Unified Software Tool for Packaging and Deploying Atomic and Distributed Analytic Pipelines . in P Solic , S Nizetic , J J P C Rodrigues , J J P C Rodrigues , D L-I Gonzalez-de-Artaza , T Perkovic , L Catarinucci & L Patrono (eds) , 2022 7th International Conference on Smart and Sustainable Technologies, SpliTech 2022 . 2022 7th International Conference on Smart and Sustainable Technologies, SpliTech 2022 , Institute of Electrical and Electronics Engineers Inc. , 7th International Conference on Smart and Sustainable Technologies, SpliTech 2022 , Split , Croatia , 5/07/22 . https://doi.org/10.23919/SpliTech55088.2022.9854211
dc.identifier.citationconference
dc.identifier.doi10.23919/SpliTech55088.2022.9854211
dc.identifier.isbn9789532901160
dc.identifier.urihttps://hdl.handle.net/11556/2381
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85138190874&partnerID=8YFLogxK
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2022 7th International Conference on Smart and Sustainable Technologies, SpliTech 2022
dc.relation.ispartofseries2022 7th International Conference on Smart and Sustainable Technologies, SpliTech 2022
dc.relation.projectIDSPRI Basque Government, ref.KK-2021/00049
dc.rightsinfo:eu-repo/semantics/restrictedAccess
dc.subject.keywordsAI life-cycle
dc.subject.keywordsanalytic pipeline
dc.subject.keywordsdeploying
dc.subject.keywordsMLOps
dc.subject.keywordspackaging
dc.subject.keywordsArtificial Intelligence
dc.subject.keywordsComputer Science Applications
dc.subject.keywordsRenewable Energy, Sustainability and the Environment
dc.subject.keywordsCivil and Structural Engineering
dc.subject.keywordsBuilding and Construction
dc.titleMLPacker: A Unified Software Tool for Packaging and Deploying Atomic and Distributed Analytic Pipelinesen
dc.typeconference output
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