MLOps vs. ModelOps: What is the difference?

Christopher

I would like to understand the difference between MLOps (Machine Learning Operations) and ModelOps in the context of managing and deploying machine learning models. While MLOps focuses on the end-to-end lifecycle of ML models—including data preparation, training, versioning, deployment, and monitoring—ModelOps often emphasizes operationalizing models at scale across the enterprise, ensuring governance, compliance, and integration with business processes. How do these two approaches differ in terms of scope, tools, and responsibilities? Additionally, in what scenarios would organizations prefer MLOps versus ModelOps, and how do both practices support collaboration between data scientists, engineers, and business teams to deliver reliable and scalable AI solutions?

Emily

MLOps and ModelOps are closely related but differ in their focus and scope. MLOps mainly covers the full lifecycle of machine learning models, including data preparation, training, versioning, deployment, and monitoring, making it more technical and development-focused. ModelOps, on the other hand, focuses on managing and governing models across the organization, ensuring they are scalable, compliant, and aligned with business goals. Organizations typically use MLOps for building and deploying models, while ModelOps is important when managing multiple models at enterprise scale. Together, they support collaboration between data scientists, engineers, and business teams, helping deliver reliable, scalable, and value-driven AI solutions.