Closed-Loop Learning

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Closed-loop learning refers to the process by which the results or outcome of AI/ML-derived resolutions or recommended remediations are fed back into the recommendations engine so that the system gets smarter over time. For example, if the system suggests a remediation action to take and the action is successful, the system then incorporates that result into the knowledge base and applies that learning in the future when the same set of conditions exist. To the extent that Closed-Loop Learning is automated (i.e. remediation actions are identified and executed by the system without human intervention), it can be viewed as a form of self healing. See also Autonomous Operations, Self-Healing Operations.

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