ModelOps is a holistic strategy to move models through the analytics life cycle quickly and iteratively so they may be deployed faster and generate desired business value, whereas, MLOps is a set of approaches for delivering and maintaining machine learning models in production in a consistent and timely manner. ModelOps is essentially a superset of MLOps with enterprise features. Data science teams benefit from MLOps technologies, but there's still a gap between the teams designing and using AI and IT executives responsible for overseeing it. So, ModelOps comes into play, justifying its potential to be so game-changing. ML only provides value once models reach production.