Multidisciplinary Teamwork in Machine Learning Operations (MLOps)

Open Access
Article
Conference Proceedings
Authors: Tapani HonkanenJonny OdwyerVesa Salminen

Abstract: Machine learning operations (MLOps) is an emerging and complex subject area involving experts from several fields and backgrounds. Its main purpose is to enable a more standardized and effective approach to building and maintaining machine learning systems. Machine learning projects have an extremely high failure rate. One of the reasons behind this is the lack of teams designed for building these systems. At the same time, machine learning projects can carry great business risks. This paper takes a scoping review approach in assessing the state of the current literature about multidisciplinary teamwork within the context of MLOps. Most of the literature reviewed on collaboration and teamwork focuses on the intimately related field of data science. These articles are analyzed, and a synthesis is presented of the gaps in the current literature for collaboration within data science. Recommendations for further research directions are given for MLOps.

Keywords: machine learning operations, multidisciplinary teamwork, process improvement

DOI: 10.54941/ahfe1002261

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