From the course: Applied AI for IT Operations (AIOps)

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Data for root cause analysis (RCA)

Data for root cause analysis (RCA) - Python Tutorial

From the course: Applied AI for IT Operations (AIOps)

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Data for root cause analysis (RCA)

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- [Instructor] In this video, I will review the input data we will use for building an RCA model. The input data is available in the file named root_cause_analysis.csv in the exercise folder. This data is collected about the symptoms seen for the problems reported. These symptoms are gathered by ITOps engineers when a problem is seen by the users. To keep it simple, each symptom is a separate attribute. That is a flag with values one or zero to indicate if the symptom is seen or not. The symptoms are CPU load, memory load, network delays, and whether some of these specific error codes were seen in the log files. the root cause attribute is the target variable that contains classes of root causes. This example uses a simple set of symptoms but in real-life scenarios there can be a large number of symptoms. Symptoms can further be broken down by where they are seen. Like for CPU load, was it at the load balancer or a specific microservice? Symptoms can also be isolated from general text…

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