AIOps in Cloud-native DevOps: IT Operations Management with Artificial Intelligence

Abstract

Businesses often deal with the growing challenge of maintaining IT operations, especially with the increase in technological dependence. Organizations adopting AIOps and organizations elevate IT operations management to address these challenges. This shift comes as a response to the increasing complexities of the huge data, applications, and infrastructure in today's digital scope. Traditionally, managing these systems incorporated manual observation and performance metric analysis, processes which are highly prone to errors and inefficiency. AIOps introduces automation through AI/ML algorithms, which
analyze patterns to predict and address issues quickly and rectify discrepancies.

Therefore, recognizing the diverse needs of companies, each facing unique goals and challenges, depicts the role of tailoring AIOps solutions to specific needs. For example, a global e-commerce company managing a huge online platform will ensure a seamless user experience and quick response to customer interactions. In such a situation, the IT team's focus with AIOps could be on real-time analytics to anticipate and mitigate potential disruptions, automated incident response to minimize downtime, and predictive maintenance to address performance issues. This article explores comprehensive insights into how AIOps functions are important in dealing with IT operations challenges within cloud-native DevOps. It aims to explore the benefits of AIOps in improving incident response efficiency, refining system monitoring capabilities, and optimizing IT operations.