Exploring Trade-Offs in MLOps Adoption
Paper i proceeding, 2023

Machine Learning Operations (MLOps) play a crucial role in the success of data science projects in companies. However, despite its obvious benefits, several companies struggle to adopt MLOps practices and face difficulty in deciding how to deploy and evolve ML models. To gain a deeper understanding of these challenges, we conduct a multi-case study involving nine practitioners from seven companies. Based on our empirical results, we identify the key trade-offs we see companies make when adopting MLOps. We categorise these trade-offs into four concerns of the BAPO model: Business, Architecture, Process, and Organisation. Finally, we provide suggestions to mitigate the identified trade-offs. By identifying and detailing these trade-offs and the implications of these, this research helps companies to ensure the successful adoption of MLOps.

MLOps

Multi-case study

BAPO model

Trade-offs

Författare

Meenu Mary John

Malmö universitet

Helena Holmström Olsson

Malmö universitet

Jan Bosch

Software Engineering 1

Erik Axel Daniel Gillblad

Chalmers, Data- och informationsteknik

Proceedings - Asia-Pacific Software Engineering Conference, APSEC

15301362 (ISSN)

Vol. 30th Asia-Pacific Software Engineering Conference, APSEC 2023 369-375
9798350344172 (ISBN)

30th Asia-Pacific Software Engineering Conference, APSEC 2023
Seoul, South Korea,

Ämneskategorier

Programvaruteknik

Systemvetenskap

Datavetenskap (datalogi)

DOI

10.1109/APSEC60848.2023.00047

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Senast uppdaterat

2024-04-23