TY - GEN
T1 - Streamlining the Operation of AI Systems
T2 - 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
AU - Weber, Michael
AU - Schniertshauer, Johannes
AU - Przybilla, Leonard
AU - Hein, Andreas
AU - Weking, Jörg
AU - Krcmar, Helmut
N1 - Publisher Copyright:
© 2024 IEEE Computer Society. All rights reserved.
PY - 2024
Y1 - 2024
N2 - Developing and operating AI systems based on machine learning (ML) has unique challenges that render traditional practices inappropriate (e.g., managing data drift). To that end, MLOps emerged as a novel paradigm for managers and teams to develop and operate such ML systems successfully. Organizations currently employ different maturity levels for MLOps, whereas higher maturity typically corresponds to more automated, streamlined, and reliable workflows. However, we have limited insight into factors influencing MLOps maturity in ML projects. Therefore, we conducted a case study on MLOps maturity in three ML projects at an automotive firm. We identified several contextual factors that facilitate or inhibit MLOps maturity, such as the ML model's complexity, the quality of new data, and the appropriateness of available MLOps tools. Our study contributes to research on managing and organizing AI by providing factors that explain the different adoption of MLOps in practice.
AB - Developing and operating AI systems based on machine learning (ML) has unique challenges that render traditional practices inappropriate (e.g., managing data drift). To that end, MLOps emerged as a novel paradigm for managers and teams to develop and operate such ML systems successfully. Organizations currently employ different maturity levels for MLOps, whereas higher maturity typically corresponds to more automated, streamlined, and reliable workflows. However, we have limited insight into factors influencing MLOps maturity in ML projects. Therefore, we conducted a case study on MLOps maturity in three ML projects at an automotive firm. We identified several contextual factors that facilitate or inhibit MLOps maturity, such as the ML model's complexity, the quality of new data, and the appropriateness of available MLOps tools. Our study contributes to research on managing and organizing AI by providing factors that explain the different adoption of MLOps in practice.
KW - Artificial Intelligence
KW - Deployment
KW - MLOps
KW - Machine Learning
KW - Operation
UR - http://www.scopus.com/inward/record.url?scp=85199777393&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:85199777393
T3 - Proceedings of the Annual Hawaii International Conference on System Sciences
SP - 5866
EP - 5875
BT - Proceedings of the 57th Annual Hawaii International Conference on System Sciences, HICSS 2024
A2 - Bui, Tung X.
PB - IEEE Computer Society
Y2 - 3 January 2024 through 6 January 2024
ER -