Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

DevOps and MLOps Integration for Data-Driven Decision-Making: Improving Business Agility and Innovation

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  • Michael Thompson
Michael Thompson
PhD, Lead Data Scientist, Innovative Solutions, Boston, USA

Published 04-10-2024

Keywords

  • DevOps,
  • MLOps
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
M. Thompson, “DevOps and MLOps Integration for Data-Driven Decision-Making: Improving Business Agility and Innovation”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 99–105, Oct. 2024, Accessed: Dec. 22, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/178

Abstract

In today’s rapidly evolving business landscape, organizations are increasingly reliant on data-driven decision-making to maintain a competitive edge. The integration of DevOps and MLOps frameworks presents a significant opportunity to enhance business agility and foster innovation. This paper explores how combining these two methodologies can streamline the deployment of machine learning models and facilitate rapid experimentation. By breaking down silos between development, operations, and data science teams, organizations can improve collaboration, accelerate delivery times, and enhance the overall quality of machine learning outputs. The paper highlights key strategies for integrating DevOps and MLOps, including the implementation of continuous integration/continuous deployment (CI/CD) pipelines, automated monitoring, and feedback loops. Furthermore, the research discusses real-world case studies that demonstrate the effectiveness of this integration in driving business outcomes. Ultimately, the paper argues that embracing a cohesive DevOps and MLOps strategy is essential for organizations seeking to leverage data for informed decision-making and sustained innovation.

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