From the course: Applied AI for IT Operations (AIOps)
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Predicting alerting - Python Tutorial
From the course: Applied AI for IT Operations (AIOps)
Predicting alerting
- [Instructor] One of the biggest challenges for ITOps is to anticipate when service disruptions may happen and take preventive steps to minimize or eliminate the downtime. Proactive alerting for upcoming disruptions will help ITOps to act and mitigate issues before disruptions may happen. There are multiple use cases in the category of predictive alerting for ITOps. They include unplanned disruptions, capacity overload, and related service degradation and assessing the impact of changes in an existing system. We will now explore the use case for identifying service disruptions in the future. We want to generate a future time series where we will be able to predict if a disruption may occur. The output will be a time series interval with a disruption flag indicating a one or a zero. What input data will we use? We want to capture input training data on our symptoms that indicate service degradation. This is a time series that captures CPU usage, memory usage, network latency…
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