AI Ops: Monitoring and Controlling AI By Aditya Abeysinghe

AI Ops: Monitoring and Controlling AI – By Aditya Abeysinghe

AI Ops: Monitoring and Controlling AI By Aditya Abeysinghe



What is AIOps?

Artificial Intelligence for IT Operations (AIOps) is a method of using Artificial Intelligence (AI) to improve operations in IT services. Before AIOps, IT operations were managed manually and any outage was solved manually. However, monitoring and management of components is difficult with use of AI in large-scale systems and in a large number of system functionalities. Therefore, automated management is used in AIOps to reduce issues with unavailability and monitor services of a system.

Why use AIOps?

Digital operations in most services today use several sources of data. Historical, real-time, network-related data etc. are some forms of data used commonly in digital services. With manual management of these services, these data sources need to be analyzed to identify issues and then minimize their impact. With large amount of data generated from these sources, most digital services need automatic analysis, logging and monitoring of data and data-related events. Therefore, automation of analysis of data and events is used with the use of AI.

With AI-based management, different types of algorithms like classification and regression are used. With these types of algorithms, classification, forecasting and identifying anomalies in data is efficient and unbiased. Different events are identified using captured data in different sources when these algorithms are deployed into systems. If any issue is detected in these events, then the component used to detect issues may notify users or may take any step to stop further issues to the system. Therefore, AIOps can detect issues and then automatically provide solutions to issues.

AIOps can also capture data real-time from multiple systems. Unlike manual data analysis where data from multiple systems need to be merged and analysed, AIOps may analyse real-time data automatically. Therefore, accuracy of algorithms is higher, data processing and analysis is faster and there are lesser errors. Therefore, AIOps can be used in real-time systems that need to process and prevent attacks with minimal issues of downtime to users.


One of the advantages of AIOps is the ability to identify issues and solve them within lesser time in comparison to identifying issues and solving them manually. This enhances its use for systems which handle critical data and minimize system downtime. AIOps can also recommend solutions to users regarding system issues using AI algorithms. Therefore, use of manual input and manual intervention in managing systems is less when AIOps is used.

Unlike managing systems manually, AI algorithms used in automatic processes can learn from previous training cycles and update algorithms to improve efficiency. Therefore, efficiency with predictions can be improved and the requirement to manually implement features is often reduced. Therefore, time taken to update algorithms and test is reduced. Therefore, effort and cost of manually testing and solving issues is reduced.

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