AIOps is a term introduced by Gartner in 2016, referring to AI for IT Operations. It combines big data and machine learning to automate IT operations processes, including event correlation, anomaly detection, and causality determination. By ODSC Community.
As more companies adopt digital transformation initiatives, we are seeing an explosion in digital systems and their associated data exhaust. By rationalizing digital signals in an intelligent way, AIOps plays a crucial role in helping companies reduce operation costs, improve engineering efficiency, and enhance customer experience. As a result, AIOps adoption has grown exponentially along with the uptick of digital transformation. Ponemon Institute© Research Report estimated that the cost-saving from AIOps systems is $17K/Outage min.
Further in the article we have:
- How to get started with Azure Metrics Advisor
- Data preparation: map your AIOps problem to the dataset
- Data onboarding
- Tuning the detection
- Root cause analysis
In addition to the detected anomalies, Azure Metrics Advisor also offers insights into what might have been the cause of the issue to help you further troubleshoot. Stakeholders can get detection results via communication channels set up by the user, along with the anomaly detected and root cause analysis. You will also find reference architecture attached. Good read!
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