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DOP 180: What is AIOps?
DevOps Paradox
Creating Clean Data and Real-Time Anomaly Detection
To ensure data cleanliness, one method is to create sterile data without anomalies or use human insights to identify normal data samples. Real-time anomaly detection without historical models can be addressed by training the system to recognize anomalies and providing feedback for corrections. For instance, in a scenario where CPU utilization spikes after code deployment, the system can learn that the spike is intentional and adjust the baseline accordingly.
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