Vol. 4 No. 2 (2024): Journal of AI-Assisted Scientific Discovery
Articles

Improving CI/CD Pipelines with MLOps-Oriented Automation for Machine Learning Models

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  • Alice Johnson
Alice Johnson
Senior Data Scientist, Tech Innovations, San Francisco, USA
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Published 09-09-2024

Keywords

  • CI/CD,
  • MLOps,
  • machine learning,
  • automation,
  • continuous integration
Creative Commons License

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

How to Cite

[1]
Alice Johnson, “Improving CI/CD Pipelines with MLOps-Oriented Automation for Machine Learning Models”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 100–106, Sep. 2024, Accessed: Nov. 16, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/176

Abstract

This paper discusses how Continuous Integration/Continuous Deployment (CI/CD) pipelines can be enhanced with MLOps-oriented automation to effectively manage the entire lifecycle of machine learning models. As organizations increasingly adopt machine learning to drive innovation and operational efficiency, the integration of MLOps principles into CI/CD pipelines becomes critical. This paper explores the various components of CI/CD pipelines and how they can be optimized for machine learning workflows. Key topics include automated testing, version control, model monitoring, and deployment strategies tailored for machine learning. By implementing MLOps-oriented automation, organizations can achieve faster model deployment, improved collaboration among teams, and enhanced model performance monitoring in production environments. This study provides insights into best practices and real-world applications, aiming to equip data science teams with the tools and knowledge necessary for seamless integration of MLOps within CI/CD frameworks.

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