Professional Insights into Benefits and Limitations of Implementing MLOps Principles

• URL: http://arxiv.org/abs/2403.13115v1
• Date: Tue, 19 Mar 2024 19:40:41 GMT
• Title: Professional Insights into Benefits and Limitations of Implementing MLOps Principles
• Authors: Gabriel Araujo, Marcos Kalinowski, Markus Endler, Fabio Calefato,
• Abstract summary: Machine Learning Operations (MLOps) has emerged as a set of practices that combines development, testing, and operations to deploy and maintain machine learning applications. We assess the benefits and limitations of using the MLOps principles in online supervised learning.
• Score: 4.188979489866561
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Context: Machine Learning Operations (MLOps) has emerged as a set of practices that combines development, testing, and operations to deploy and maintain machine learning applications. Objective: In this paper, we assess the benefits and limitations of using the MLOps principles in online supervised learning. Method: We conducted two focus group sessions on the benefits and limitations of applying MLOps principles for online machine learning applications with six experienced machine learning developers. Results: The focus group revealed that machine learning developers see many benefits of using MLOps principles but also that these do not apply to all the projects they worked on. According to experts, this investment tends to pay off for larger applications with continuous deployment that require well-prepared automated processes. However, for initial versions of machine learning applications, the effort taken to implement the principles could enlarge the project's scope and increase the time needed to deploy a first version to production. The discussion brought up that most of the benefits are related to avoiding error-prone manual steps, enabling to restore the application to a previous state, and having a robust continuous automated deployment pipeline. Conclusions: It is important to balance the trade-offs of investing time and effort in implementing the MLOps principles considering the scope and needs of the project, favoring such investments for larger applications with continuous model deployment requirements.

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