Computer infrastructure has become more complex and dispersed, resulting in a flood of alerts in IT operations centers. As the volumes grow, MSPs and enterprises can become overwhelmed and challenged by troubleshooting routine problems.
Enter artificial intelligence for IT operations (AIOps), which applies AI and machine learning software to solve the problem by automating and streamlining significant parts of the management process.
The modern data center often looks like a NASA spacecraft control room with large wall panels and teams of data center technicians banging away at keyboards to glean how the computer infrastructure is performing and why. Such vital deductions have become more difficult to discern.
Organizations must have a strong ecosystem to construct an AI model because it is a complex process. According to Gartner, the model includes five stages:
Ingestion. Collect, index and normalize event and telemetry data from multiple domains.
Topology. Build software that visually displays activity within the computer infrastructure.
Correlation. Connect events across domains and sources, ideally without much human intervention.
Recognition. Understand the current state of enterprise infrastructure performance and how it will change.
Remediation. Train AI tools to take action or make recommendations when problems arise.