Advancing ML model operationalization: Lessons from enterprise ML Ops

As Machine Learning (ML) becomes increasingly embedded into enterprise workflows, organizations are recognizing the critical need for robust and scalable MLOps (Machine Learning Operations) frameworks. This review synthesizes leading practices, architectures, and tools for operationalizing ML models across industries. Drawing on empirical studies and industry insights, the paper explores the challenges of model versioning, deployment, monitoring, and governance at scale. A proposed theoretical model highlights closed-loop retraining and compliance-driven design. Through comparative performance results and platform benchmarking, this work provides a blueprint for enterprises seeking to accelerate ML adoption while preserving reliability, explainability, and agility.