The MLOps Approach to Model Deployment: A Road Map to Seamless Scalability

Abstract

The operational problems of deploying Machine Learning (ML) models at scale are the focal point of the research, which explores the complex world of MLOps. To determine which operational platforms are most effective in managing deployment pipelines, the research examines MLflow, Kubeflow, and Airflow, among others. Focusing on version management and reproducibility, the article explores methods and resources used to guarantee the longterm viability and traceability of models that have been put into use. This study delves into the incorporation of Continuous Integration and Continuous Deployment (CI/CD) pipelines into MLOps processes, which are crucial for attaining operational effectiveness. Case studies demonstrate how Continuous Integration/Continuous Deployment approaches have helped with deployment constraints and joint development in real-world settings. Common operational issues in MLOps are also covered in the investigation, including dealing with dropping performance, increasing dependencies, and data drift management. Presenting practical insights via a scientific perspective, this work attempts to guide MLOps practitioners as they navigate the ever-changing
operational environment of large-scale model deployment.