[O.CM.10] Proactively detect issues using AI/ML
Category: OPTIONAL
Adopt data-driven AI/ML monitoring tools and techniques like Artificial Intelligence Operations (AIOps), ML-powered anomaly detection, and predictive analytics solutions, to detect issues and performance bottlenecks proactively—even before system performance is impacted.
Choose a tool that can leverage data and analytics to automatically infer predictions, and begin to feed data to it and inject failure to test the validity of the tool. These tools should have access to both historical and real-time data. Once operational, the tool can automatically detect issues, predict impending resource exhaustion, detail likely causes, and recommend remediation actions to the team. Ensure that there is a feedback loop to continuously train and refine these models based on real-world data and incidents.
Start small when setting up alerts from these tools to avoid alert fatigue and maintain trust in the system. As the tool becomes more familiar with the data patterns, teams can gradually increase the alerting scope. Regularly validate the tool's predictions by injecting failures and observing the responses.
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