Published 09-09-2024
Keywords
- CI/CD,
- MLOps,
- machine learning,
- automation,
- continuous integration
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
How to Cite
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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References
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