Deploying Real-Time Machine Learning Models in DevOps Environments: A Hybrid MLOps Approach

Abstract

The integration of real-time machine learning (ML) models into DevOps environments has emerged as a critical capability for organizations seeking to enhance their operational efficiency and data-driven decision-making. This paper discusses a hybrid approach to Machine Learning Operations (MLOps) that facilitates the seamless deployment of real-time ML models in DevOps settings. By synthesizing best practices from both traditional DevOps and advanced ML workflows, this approach aims to minimize downtime, ensure scalability, and maintain robust performance in production environments. Key components of this hybrid model include continuous integration and deployment (CI/CD) pipelines, monitoring and feedback mechanisms, and the use of containerization technologies. The paper further examines the challenges associated with deploying real-time ML models, such as data drift, model performance tracking, and infrastructure management. Through case studies and practical applications, the findings underscore the importance of adopting a hybrid MLOps strategy to effectively bridge the gap between ML development and operational deployment.