Advancing Machine Learning Operations (MLOps): A Framework for Continuous Integration and Deployment of Scalable AI Models in Dynamic Environments

Abstract

The rapid expansion of artificial intelligence (AI) applications has intensified the need for efficient and scalable Machine Learning Operations (MLOps) frameworks to streamline the deployment and lifecycle management of machine learning (ML) models. This study proposes a comprehensive MLOps framework that integrates continuous integration (CI), continuous deployment (CD), automated monitoring, and rollback mechanisms to support the scalable deployment of AI models in dynamic environments. Utilizing a cloud-native architecture built on tools such as Jenkins, Docker, Kubernetes, MLflow, and Airflow, the framework was tested across multiple model types and evaluated using both technical and operational performance metrics. Results show significant improvements in model accuracy, deployment latency, rollback speed, and drift detection compared to baseline systems and industry averages. The framework achieved a 92.8% model accuracy, reduced deployment time by over 65%, and improved rollback efficiency by 95%. A comparative analysis of tool integration and pipeline performance further validated the system’s scalability, flexibility, and resilience. The findings demonstrate the framework’s ability to bridge the gap between experimentation and production, making it a practical and powerful solution for real-time, high-demand AI applications. This study offers valuable insights for researchers and practitioners seeking to enhance the robustness and efficiency of AI deployment in ever-evolving environments.