AI/ML Operationalization

The process of deploying and maintaining AI/ML models in real-world environments to ensure consistent performance and integration with business workflows.

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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