Cross-platform edge deployment of machine learning models: a model-driven approach.
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| Title: | Cross-platform edge deployment of machine learning models: a model-driven approach. |
|---|---|
| Authors: | Karlsson Landgren, Albin1 (AUTHOR) albk@student.chalmers.se, Perhult Johnsen, Philip1 (AUTHOR) perhult@student.chalmers.se, Strüber, Daniel1,2 (AUTHOR) danstru@chalmers.se |
| Source: | Software & Systems Modeling. Feb2026, Vol. 25 Issue 1, p163-187. 25p. |
| Subjects: | Model-driven software architecture, Edge computing, Software libraries (Computer programming), Machine learning, Domain-specific programming languages |
| Abstract: | Deploying machine learning (ML) models on edge devices presents unique challenges, arising from the different environments used for developing ML models and those required for their deployment, leading to a gray area of competence and expertise between ML engineers and application developers. In this paper, we explore the use of model-driven engineering to simplify the deployment of ML models on edge devices, specifically smartphones. We present a DSL for the specification of the ML serving pipelines (pre- and postprocessing of data before and after inference), together with a model interpretation approach that allows to make changes to the pipeline during runtime, thus removing the need to re-release an application upon changes to a pipeline. We followed a design science approach, in which we elicited requirements through an initial artifact study and interviews with engineers at an industrial partner. This was followed by the design and implementation of a lightweight, JSON-based domain-specific language designed to describe ML serving pipelines, along with an accompanying Flutter library to execute the pipelines during runtime. A preliminary evaluation with four developers shows the potential of this approach to increase development speed, decrease the amount of code required to make changes to an ML serving pipeline, and make less-experienced engineers more confident contributing to the domain. [ABSTRACT FROM AUTHOR] |
| Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 192012042 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Cross-platform edge deployment of machine learning models: a model-driven approach. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Karlsson Landgren%2C+Albin%22">Karlsson Landgren, Albin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> albk@student.chalmers.se</i><br /><searchLink fieldCode="AR" term="%22Perhult Johnsen%2C+Philip%22">Perhult Johnsen, Philip</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> perhult@student.chalmers.se</i><br /><searchLink fieldCode="AR" term="%22Strüber%2C+Daniel%22">Strüber, Daniel</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<i> danstru@chalmers.se</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Software+%26+Systems+Modeling%22">Software & Systems Modeling</searchLink>. Feb2026, Vol. 25 Issue 1, p163-187. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Model-driven+software+architecture%22">Model-driven software architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Edge+computing%22">Edge computing</searchLink><br /><searchLink fieldCode="DE" term="%22Software+libraries+%28Computer+programming%29%22">Software libraries (Computer programming)</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Domain-specific+programming+languages%22">Domain-specific programming languages</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Deploying machine learning (ML) models on edge devices presents unique challenges, arising from the different environments used for developing ML models and those required for their deployment, leading to a gray area of competence and expertise between ML engineers and application developers. In this paper, we explore the use of model-driven engineering to simplify the deployment of ML models on edge devices, specifically smartphones. We present a DSL for the specification of the ML serving pipelines (pre- and postprocessing of data before and after inference), together with a model interpretation approach that allows to make changes to the pipeline during runtime, thus removing the need to re-release an application upon changes to a pipeline. We followed a design science approach, in which we elicited requirements through an initial artifact study and interviews with engineers at an industrial partner. This was followed by the design and implementation of a lightweight, JSON-based domain-specific language designed to describe ML serving pipelines, along with an accompanying Flutter library to execute the pipelines during runtime. A preliminary evaluation with four developers shows the potential of this approach to increase development speed, decrease the amount of code required to make changes to an ML serving pipeline, and make less-experienced engineers more confident contributing to the domain. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Software & Systems Modeling is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=192012042 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s10270-025-01273-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 163 Subjects: – SubjectFull: Model-driven software architecture Type: general – SubjectFull: Edge computing Type: general – SubjectFull: Software libraries (Computer programming) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: Domain-specific programming languages Type: general Titles: – TitleFull: Cross-platform edge deployment of machine learning models: a model-driven approach. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Karlsson Landgren, Albin – PersonEntity: Name: NameFull: Perhult Johnsen, Philip – PersonEntity: Name: NameFull: Strüber, Daniel IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 02 Text: Feb2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 16191366 Numbering: – Type: volume Value: 25 – Type: issue Value: 1 Titles: – TitleFull: Software & Systems Modeling Type: main |
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