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

Achieving End-to-End Automation: Combining DevOps and MLOps for Streamlined Data and Model Workflows

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  • Alexandra Thompson
Alexandra Thompson
PhD, Senior Research Scientist, Department of Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA

Published 18-09-2024

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]
A. Thompson, “Achieving End-to-End Automation: Combining DevOps and MLOps for Streamlined Data and Model Workflows”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 62–68, Sep. 2024, Accessed: Nov. 06, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/173

Abstract

The growing complexity of machine learning (ML) workflows and data engineering has necessitated the integration of DevOps practices with MLOps to achieve end-to-end automation. This paper explores the convergence of these two methodologies, highlighting how they can streamline data and model workflows for enhanced efficiency, collaboration, and continuous delivery. By analyzing the key components of DevOps and MLOps, this study identifies best practices and tools that facilitate seamless integration, ultimately fostering a culture of collaboration between data scientists and IT operations teams. Furthermore, the paper discusses the benefits of end-to-end automation, including improved model performance, faster deployment cycles, and enhanced reproducibility. The findings underscore the importance of adopting a holistic approach to automation in data and ML workflows, paving the way for organizations to leverage the full potential of their data assets.

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