MLOps vs. ModelOps: What is the difference?

Sarah

I would like to understand the difference between MLOps (Machine Learning Operations) and ModelOps in the context of deploying and managing AI/ML models. While MLOps focuses on the end-to-end lifecycle of machine learning models—including data preparation, model training, versioning, deployment, monitoring, and continuous improvement—ModelOps often emphasizes operationalizing models across enterprise applications, ensuring governance, compliance, scalability, and integration with business workflows. How do these approaches differ in their scope, tools, and processes, and in what situations would organizations prioritize MLOps versus ModelOps? Additionally, how do both practices support collaboration between data scientists, DevOps engineers, and business stakeholders to ensure models are reliable, compliant, and delivering real business value?

Christopher

MLOps and ModelOps are closely related but differ in scope and focus. MLOps mainly handles the end-to-end lifecycle of machine learning models, including data preparation, training, versioning, deployment, and monitoring. ModelOps, on the other hand, focuses more on managing and governing models across the enterprise, ensuring scalability, compliance, and integration with business processes. While MLOps is often used by data science and engineering teams to build and maintain models, ModelOps is important for organizations that need to manage multiple models in production at scale. Together, they support collaboration between data scientists, DevOps engineers, and business teams, ensuring models are reliable, compliant, and deliver real business value.