Copyright © 2026 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

@article{200147,
        author = {Kalpana Sonval and Aditya Hambre and Yash Kadav and Arti khande},
        title = {Automating ML Model Deployment Using CI/CD Pipelines with Performance Monitoring},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {12},
        pages = {625-627},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200147},
        abstract = {The rapid adoption of Machine Learning (ML) in critical applications has created a pressing need for efficient deployment, monitoring, and lifecycle management systems. Traditional deployment practices are often manual, time-consuming, and prone to inconsistencies. Recent research highlights the importance of integrating Machine Learning Operations (MLOps) with Continuous Integration and Continuous Deployment (CI/CD) pipelines to automate the end-to-end lifecycle of ML models. This paper proposes a comprehensive framework for automating ML model deployment using CI/CD pipelines combined with real-time performance monitoring.
The framework incorporates model versioning, automated testing, containerization, and continuous monitoring to ensure model reliability and scalability. Experimental studies show that integrating MLOps with CI/CD can significantly reduce deployment time (up to 94%) and improve model accuracy and efficiency . Additionally, ensemble learning techniques are explored to enhance model performance before deployment. The study demonstrates how automated pipelines and monitoring systems address challenges such as model drift, scalability, and reproducibility, thereby improving the overall robustness of ML systems.},
        keywords = {},
        month = {May},
        }