Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/327539 
Year of Publication: 
2025
Citation: 
[Journal:] Journal of Innovation & Knowledge (JIK) [ISSN:] 2444-569X [Volume:] 10 [Issue:] 1 [Article No.:] 100637 [Year:] 2025 [Pages:] 1-16
Publisher: 
Elsevier, Amsterdam
Abstract: 
The field of MLOps (Machine Learning Operations), which focuses on effectively managing and operationalizing ML workflows, has grown because of the advancements in machine learning (ML). The goal of this study is to examine and contrast the difficulties encountered in the implementation of MLOps in enterprises with those encountered in DevOps. An SLR (Systematic Literature Review) is the first step in the research process to find the issues raised in the literature. The results of this study are based on qualitative content analysis using grounded theory and semi-structured interviews with 12 ML practitioners from different sectors. Organisational, technical, operational, and business problems are the four distinct aspects of challenges for MLOps that our study highlights. These challenges are further defined by eleven different themes. Our research indicates that while some issues, such as data and model complexity, are unique to MLOps, others are shared by DevOps and MLOps as well. The report offers suggestions for further research and summarises the difficulties.
Subjects: 
Machine learning operations (MLOps)
Grounded theory
Data science
JEL: 
C88
D83
L86
L17
O32
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article

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