Model Ops
Operationalize your existing models, automate the run against new data.
Life cycle management of your AI models with AI supporting also automation.
The maturity of your organization is assessed and our Consultants lead the way in each step to gradually achieve your business goals and sophistication levels.
Operationalize your existing models, automate the run against new data.
Life cycle management of your AI models with AI supporting also automation.
The maturity of your organization is assessed and our Consultants lead the way in each step to gradually achieve your business goals and sophistication levels.
is the process of managing ML models throughout their lifecycle at an enterprise scale. ModelOps is considered as a superset of MLOps, which refers to the processes involved to operationalize and manage AI models in use in production systems. The advantage of ModelOps over MLOps is that MLOps focuses on the machine learning models only, whereas Modelops is focused to operationalize all AI models.
The organization looking to set up ModelOps should set up MLOps first before moving on to ModelOps. The skills required for ModelOps are the same as MLOps, with some additional skills pertaining to the entire gamut of AI.
