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Titel:

Streamlining the Operation of AI Systems: Examining MLOps Maturity at an Automotive Firm

Dokumenttyp:
Konferenzbeitrag
Art des Konferenzbeitrags:
Textbeitrag / Aufsatz
Autor(en):
Weber, Michael; Schniertshauer, Johannes; Przybilla, Leonard; Hein, Andreas; Weking, Jörg; Krcmar, Helmut
Abstract:
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 fac...     »
Kongress- / Buchtitel:
57th Hawaii International Conference on System Sciences
Jahr:
2024
Jahr / Monat:
2024-01
Monat:
Jan
Reviewed:
ja
Sprache:
en
 BibTeX