Mapeo Sistemático de un Proceso para Soportar MLOps en las Pequeñas y Medianas Empresas de Desarrollo de Software

Abstract

Introduction: Currently, software development companies (SDCs) have begun to incorporate Machine Learning into their projects, which has allowed Machine Learning models to move from the experimentation stage to the production stage. This is where Machine Learning Operations (MLOps) comes in, with the goal of bridging the gap between operations, development, and data science teams.

Objective: Conduct research on the current state of knowledge regarding the adoption of MLOps in SDCs through systematic literature mapping, with the aim of studying, identifying, and understanding initiatives, solutions, and issues related to work in this area.

Method: A systematic literature mapping was carried out using a defined protocol, which included the development of research questions and the implementation of a search strategy in six databases. Primary articles were then selected based on pre-established inclusion and exclusion criteria. The research questions were addressed based on the results obtained, allowing the findings to be classified and characterized. Finally, the results were analyzed, and the corresponding conclusions were presented.

Results: The findings of this article reflect the efforts of the scientific community to define the principles, roles, artifacts, technologies, challenges, and crucial factors for the implementation of Machine Learning Operations in SDCs.

Conclusions: The research questions are answered, which reveal the main challenges in implementing Machine Learning.