What is AIOps and what problems does it solve for large-scale IT operations?

As systems grow into hundreds of services with thousands of metrics and alert rules, two problems compound: alert fatigue (too many alerts, many redundant or low-value, causing responders to tune out or miss real incidents) and diagnosis time (a single root cause can trigger dozens of downstream alerts across unrelated-looking systems, and finding the actual origin manually is slow). Static, per-metric thresholds don't scale to this — they either fire too often (noisy) or miss real problems (too lenient), and they can't see relationships across signals.

AIOps platforms ingest metrics, logs, traces, and change/deployment events, and apply techniques like anomaly detection (flagging unusual behavior without a hand-tuned threshold), event correlation/clustering (grouping the flood of alerts from one root cause into a single incident), and causal or topology-aware analysis (using service dependency maps to suggest where a problem likely originated). The output is meant to compress a large volume of raw signals into a small number of actionable, prioritized incidents with supporting evidence — the human still decides on and executes the fix.