As a means of increasing agility and scalability, many organizations are adopting multicloud environments and rearchitecting their applications into microservices and containers. The cloud environment, however, introduces new challenges. It contains hundreds of technologies, millions of lines of code, and billions of dependencies.
In addition, many organisations still rely on DevOps teams to manually monitor these environments and resolve issues. It is impossible for humans to keep up with the frequency and magnitude of change in an ever-evolving cloud environment.In order to deal with these complexities, many organizations use artificial intelligence for their operations.
The main goal of AIOps is to automate IT operations processes involving big data and machine learning. Examples include event correlation, anomaly detection, and causality determination. The capabilities of AIOps can transform digital strategies, but they aren't suitable for every situation. The best AIOps strategy is developed by clarifying the issues that need to be solved and the best AI approach.