Cloud-Native Countinous Integration/Continous Deployment (CI/CD) Pipeline

This paper explores the design and implementation of a cloud-native Continuous Integration/Continuous Deployment (CI/CD) pipeline for automating the deployment of machine learning models. Leveraging modern technologies like Docker, Kubernetes, Jenkins, and cloud platforms such as AWS, Google Cloud, and Azure, the pipeline enhances the efficiency, consistency, and reliability of the development process. Key challenges such as scalability, security, and model drift are addressed, offering solutions to ensure smooth operations in dynamic production environments. Performance evaluations demonstrate the benefits of a cloud-native approach, highlighting improvements in deployment speed and resource optimization. The paper concludes by discussing future directions for further automation and advancements in cloud-native CI/CD processes for machine learning.