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Developing ML/DL Models: A Design Framework
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). (Software Center)ORCID iD: 0000-0003-3972-2265
Malmö University, Faculty of Technology and Society (TS), Department of Computer Science and Media Technology (DVMT). (Software Center)ORCID iD: 0000-0002-7700-1816
Chalmers University of Technology. (Software Center)
2020 (English)In: Proceedings 2020 IEEE/ACM International Conferenceon Software and System Processes ICSSP 2020, ACM Digital Library, 2020, p. 1-10Conference paper, Published paper (Refereed)
Abstract [en]

Artificial Intelligence is becoming increasingly popular with organizations due to the success of Machine Learning and Deep Learning techniques. Using these techniques, data scientists learn from vast amounts of data to enhance behaviour in software-intensive systems. Despite the attractiveness of these techniques, however, there is a lack of systematic and structured design process for developing ML/DL models. The study uses a multiple-case study approach to explore the different activities and challenges data scientists face when developing ML/DL models in software-intensive embedded systems. In addition, we have identified seven different phases in the proposed design process leading to effective model development based on the case study. Iterations identified between phases and events which trigger these iterations optimize the design process for ML/DL models. Lessons learned from this study allow data scientists and engineers to develop high-performance ML/DL models and also bridge the gap between high demand and low supply of data scientists.

Place, publisher, year, edition, pages
ACM Digital Library, 2020. p. 1-10
Keywords [en]
Machine Learning, Deep Learning, Artificial Intelligence, Design, Software Engineering
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:mau:diva-17137DOI: 10.1145/3379177.3388892ISI: 001039139300001Scopus ID: 2-s2.0-85092522299ISBN: 978-1-4503-7512-2 (print)OAI: oai:DiVA.org:mau-17137DiVA, id: diva2:1426930
Conference
ICSSP '20: International Conference on Software and System Processes, June 26-28, 2020, Seoul, Republic of Korea
Available from: 2020-04-28 Created: 2020-04-28 Last updated: 2023-12-13Bibliographically approved
In thesis
1. Design Methods and Processes for ML/DL models
Open this publication in new window or tab >>Design Methods and Processes for ML/DL models
2021 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Context: With the advent of Machine Learning (ML) and especially Deep Learning (DL) technology, companies are increasingly using Artificial Intelligence (AI) in systems, along with electronics and software. Nevertheless, the end-to-end process of developing, deploying and evolving ML and DL models in companies brings some challenges related to the design and scaling of these models. For example, access to and availability of data is often challenging, and activities such as collecting, cleaning, preprocessing, and storing data, as well as training, deploying and monitoring the model(s) are complex. Regardless of the level of expertise and/or access to data scientists, companies in all embedded systems domain struggle to build high-performing models due to a lack of established and systematic design methods and processes.

Objective: The overall objective is to establish systematic and structured design methods and processes for the end-to-end process of developing, deploying and successfully evolving ML/DL models.

Method: To achieve the objective, we conducted our research in close collaboration with companies in the embedded systems domain using different empirical research methods such as case study, action research and literature review.

Results and Conclusions: This research provides six main results: First, it identifies the activities that companies undertake in parallel to develop, deploy and evolve ML/DL models, and the challenges associated with them. Second, it presents a conceptual framework for the continuous delivery of ML/DL models to accelerate AI-driven business in companies. Third, it presents a framework based on current literature to accelerate the end-to-end deployment process and advance knowledge on how to integrate, deploy and operationalize ML/DL models. Fourth, it develops a generic framework with five architectural alternatives for deploying ML/DL models at the edge. These architectural alternatives range from a centralized architecture that prioritizes (re)training in the cloud to a decentralized architecture that prioritizes (re)training at the edge. Fifth, it identifies key factors to help companies decide which architecture to choose for deploying ML/DL models. Finally, it explores how MLOps, as a practice that brings together data scientist teams and operations, ensures the continuous delivery and evolution of models. 

Place, publisher, year, edition, pages
Malmö: Malmö universitet, 2021. p. 204
Series
Studies in Computer Science ; 17
Keywords
Machine Learning, Deep Learning, Development, Deployment, Evolution
National Category
Computer Systems
Identifiers
urn:nbn:se:mau:diva-45026 (URN)10.24834/isbn.9789178771998 (DOI)978-91-7877-198-1 (ISBN)978-91-7877-199-8 (ISBN)
Presentation
(English)
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Supervisors
Note

Due to copyright reasons, the articles are not included in the fulltext online

Available from: 2021-08-20 Created: 2021-08-20 Last updated: 2024-03-26Bibliographically approved

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John, Meenu MaryOlsson, Helena Holmström

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