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The goldilocks framework: towards selecting the optimal approach to conducting AI projects
McDermott, The Hague, The Netherlands.
Chalmers University of Technology, Gothenburg, Sweden.
Malmö universitet, Fakulteten för teknik och samhälle (TS), Institutionen för datavetenskap och medieteknik (DVMT).ORCID-id: 0000-0002-7700-1816
2022 (Engelska)Ingår i: CAIN '22: Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, ACM Digital Library, 2022, s. 124-135Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Artificial intelligence is increasingly becoming important to businesses since many companies have realized the benefits of applying Machine Learning (ML) and Deep Learning (DL) into their operations. Nevertheless, ML/DL technologies' industrial development and deployment examples are still rare and generally confined within a small cluster of large international companies who are struggling to apply ML more broadly and deploy their use cases at a large scale. Meanwhile, current AI market has started offering various solutions and services. Thus, organizations must understand how to acquire AI technology based on their business strategy and available resources. This paper discusses the industrial experience of developing and deploying ML/DL use cases to support organizations in their transformation towards AI. We identify how various factors, like cost, schedule, and intellectual property, can be affected by the choice of approach towards ML/DL project development and deployment within large international engineering corporations. As a research result, we present a framework that covers the trade-offs between those various factors and can support engineering companies to choose the best approach based on their long-term business strategies and, therefore, would help to accomplish their ML/DL project deployment successfully.  

 

Ort, förlag, år, upplaga, sidor
ACM Digital Library, 2022. s. 124-135
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URN: urn:nbn:se:mau:diva-56423DOI: 10.1145/3522664.3528595Scopus ID: 2-s2.0-85133479649ISBN: 978-1-4503-9275-4 (tryckt)OAI: oai:DiVA.org:mau-56423DiVA, id: diva2:1715693
Konferens
CAIN '22: 1st Conference on AI Engineering - Software Engineering for AI Pittsburgh Pennsylvania May 16 - 24, 2022
Tillgänglig från: 2022-12-02 Skapad: 2022-12-02 Senast uppdaterad: 2024-02-05Bibliografiskt granskad

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Olsson, Helena Holmström

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Totalt: 73 träffar
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