The term ModelOps refers to the governance and life cycle management of all artificial intelligence and decision models, including models based on machine learning, knowledge graphs, rules, optimization, linguistics and agents, according to Gartner.
“In contrast to MLOps, which focuses only on the operationalization of ML models, and AIOps which is AI for IT Operations, ModelOps focuses on the operationalization of all AI and decision models,” Gartner says.
As this video suggests, ModelOps aims to support scaling of artificial intelligence capabilities across an enterprise.
Some of you might see this as an example of an important business strategy, which is to create a new market. That is one way to avoid being “trapped” in battle aimed at taking market share from other competitors, and instead boosting value and uniqueness by competing in a new and different market.
It is vendor push, to be sure, but such pushes--as opposed to buyer pulls-- fail if it does not align with an enterprise's need to solve a real problem important to its business model.
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