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Advancing MLOps fro...
Advancing MLOps from Ad hoc to Kaizen
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- John, Meenu Mary (författare)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT)
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- Gillblad, Daniel (författare)
- Chalmers University of Technology & AI Sweden,Gothenburg,Sweden
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- Olsson, Helena Holmström (författare)
- Malmö universitet,Institutionen för datavetenskap och medieteknik (DVMT)
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- Bosch, Jan (författare)
- Chalmers University of Technology,Computer Science and Engineering,Gothenburg,Sweden
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(creator_code:org_t)
- Institute of Electrical and Electronics Engineers (IEEE), 2023
- 2023
- Engelska.
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Ingår i: 2023 49th Euromicro Conference on Software Engineering and Advanced Applications (SEAA). - : Institute of Electrical and Electronics Engineers (IEEE). - 9798350342352 - 9798350342369
- Relaterad länk:
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https://urn.kb.se/re...
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https://doi.org/10.1...
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Abstract
Ämnesord
Stäng
- Companies across various domains increasingly adopt Machine Learning Operations (MLOps) as they recognise the significance of operationalising ML models. Despite growing interest from practitioners and ongoing research, MLOps adoption in practice is still in its initial stages. To explore the adoption of MLOps, we employ a multi-case study in seven companies. Based on empirical findings, we propose a maturity model outlining the typical stages companies undergo when adopting MLOps, ranging from Ad hoc to Kaizen. We identify five dimensions associated with each stage of the maturity model as part of our MLOps framework. We also map these seven companies to the identified stages in the maturity model. Our study serves as a roadmap for companies to assess their current state of MLOps, identify gaps and overcome obstacles to successfully adopting MLOps.
Ämnesord
- NATURVETENSKAP -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)
- NATURAL SCIENCES -- Computer and Information Sciences -- Computer Sciences (hsv//eng)
Publikations- och innehållstyp
- ref (ämneskategori)
- kon (ämneskategori)
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