ModelOps(Model Operations)

There is a very thin line between ModelOps and MLOps. ModelOps is the next stage of MLOps where focus is more towards automating, retraining, and maturing models on its own.

MLOps mainly focuses on model management, model monitoring and CI/ CD whereas ModelOps mainly focuses on addressing and sharing AI performance to the stakeholders without the need of Data Science and Machine Learning Team members.

Few differences between ModelOps and MLOps.

• MLOps focuses on ML models operationalization whereas ModelOps focuses on decision models and AI operationalization.
• MLOps focuses on continuous cycle of development, implementation, and monitoring of models whereas ModelOps focuses on life cycle management and governance of models.
• MLOps focuses on creating models for AI enabled applications whereas ModelOps focuses on AI transparency and usage of KPIs by business users.

In nutshell, ModelOps supports AI capabilities where involvement of Data Science become less and less as Models run on their own, retrain on their own, deploy on their own, extract business KPIs on their own etc.