MLOps vs. ModelOps: What is the difference?

AaravSingh

I would like to understand the difference between MLOps and ModelOps, as both focus on managing machine learning models but seem to have different scopes. While MLOps is mainly about building, deploying, and maintaining ML models in production, ModelOps appears to take a broader approach by managing the entire lifecycle of all types of models across an organization—so how do these two approaches differ in practice? Additionally, what are the key use cases, tools, and skills required for each, and how do organizations decide which approach to adopt?

Sarah

MLOps and ModelOps both deal with managing machine learning models, but they differ in scope and focus. MLOps mainly focuses on the technical lifecycle of ML models, including building, deploying, and monitoring them in production. ModelOps takes a broader, business-level approach by managing multiple models across the organization, ensuring governance, compliance, and integration with business processes. In practice, MLOps is used by data science and engineering teams, while ModelOps is important for enterprise-wide model management. Organizations choose MLOps for development and deployment needs, and ModelOps when they need to manage models at scale, with both approaches helping deliver reliable and valuable AI solutions.