Many operations staff are concerned with the automation of too much of their organization’s monitoring and management functionality. This concern is based on two parts: concern that automated systems might miss something important, and concern that this is a step toward automating away jobs.
The current state of AI/MLOps is such that the systems are not fully responsible for response, making both of these points somewhat moot. AIOps aims to reduce the amount of noise that operations or DevOps teams must work through while watching for true issues in complex systems. This has the effect of notifications and issues still being raised to technologists, but fewer “false positive”-type alerts. That frees time that was literally wasted so team members can focus on real issues.
The ability to correlate disparate bits of data into a picture of an emerging issue is starting to appear in these systems also—but again, team members are then faced with a real problem that they must spend their time resolving instead of attempting to identify root causes. If an API reports degradation, and a server the API uses reports degradation, and IOps on the server report degradation … all of these are likely tied to disk or controller issues. AIOps points operators to the drive without them having to walk through a long chain of events to make that determination.
