Building Unified ML Pipelines: Convergence of DevOps and MLOps in Enterprise Environments

The artificial distinction between DevOps and MLOps creates significant friction in production machine learning systems. Organizations deploying enterprise ML applications must integrate model training, serving, and versioning into unified CI/CD pipelines alongside traditional application code to achieve operational efficiency and governance compliance. This paper presents comprehensive architectural patterns for converging DevOps and MLOps practices, with particular emphasis on feature stores as the foundation for eliminating training-serving skew, data lineage systems for end-to-end provenance tracking, and GitOps-based model deployment for reproducible releases.