AUTOMATED MODEL TESTING AND VALIDATION IN CI/CD PIPELINES FOR AI APPLICATIONS

Authors

SWAMY PRASADARAO VELAGA

Abstract

Automated model testing and validation within Continuous Integration and Continuous Delivery (CI/CD) pipelines for AI and machine learning (ML) applications play a pivotal role in ensuring their reliability, performance, and ethical compliance. This paper explores current methodologies and best practices in integrating automated testing frameworks tailored for AI models, encompassing techniques such as unit testing, integration testing, performance evaluation, and fairness assessment. We highlight the importance of these practices in mitigating risks associated with model deployment and improving overall application quality. Furthermore, the paper discusses future research directions, including advanced testing techniques for specific AI domains, integration with emerging technologies like federated learning and differential privacy, and strategies for ethical and regulatory compliance. By addressing these challenges and opportunities, this research aims to advance the field of automated model testing in CI/CD pipelines, facilitating the development of more robust, scalable, and ethically sound AI applications across diverse industries..

Keywords

Continuous Integration, Continuous Deployment, Machine Learning, CI/CD Pipelines

AUTOMATED MODEL TESTING AND VALIDATION IN CI/CD PIPELINES FOR AI APPLICATIONS. SWAMY PRASADARAO VELAGA. 2017. IJIRCT, Volume 3, Issue 6. Pages 1-9. https://www.ijirct.org/viewPaper.php?paperId=2407062