Operationalization

The process of moving AI and machine learning models from development into active use within business applications. It involves deployment, monitoring, and governance to ensure models produce reliable, repeatable, and compliant outputs.

It involves automating and scaling model lifecycle activities—from development and deployment to monitoring, remediation, and retirement—to ensure models deliver consistent, reliable business value.

ModelOp defines operationalization through the lens of ModelOps, a broader discipline that manages all types of decision models, including ML, rules-based systems, optimization models, and knowledge graphs. Key components include governance, automation, risk management, and integration with IT systems and data platforms.

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