From the course: Applied AI for IT Operations (AIOps)
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Predicting root causes with Keras - Python Tutorial
From the course: Applied AI for IT Operations (AIOps)
Predicting root causes with Keras
- [Instructor] Now that we have built an RCA model, let's use it to predict the root cause for a new incident. When a new incident happen, we typically identify the symptoms of the incident first, and populate the related feature variables here, like CPU load, memory load, delays and error codes. We then pass these as an array to the model's predict classes function. This function will return a numeric value for the root cause. We then translate the numeric value into a label using the inverse transform function on the encoder. Let's execute this code and view the results. As we can see, it predicts the root cause to be a database issue. It is possible to use this model to predict root causes for a batch of incidents. Batch predictions are a lot more efficient than executing them one by one. To do this, we create an array of arrays with the inside array, representing a row of feature variables. On calling the predict classes method on the model, we get a list of numeric predictions…
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