AIOps ("Artificial Intelligence for IT Operations") is the application of machine learning and, increasingly, large language models (LLMs) to observability telemetry — metrics, logs, traces, and events — in order to detect anomalies, reduce alert noise, correlate signals, and localize root causes faster than manual operation sallow. This note surveys the principal method families for anomaly detection, clustering and correlation, and root-cause analysis (RCA), then traces developments from 2021–2025:transformer and foundation models for time series, LLM-based log/telemetry analysis,eBPF-native instrumentation, and the consolidation of OpenTelemetry as the vendor-neutral standard. Each substantive technical claim is tied to a primary source (arXiv ID/DOI orofficial docs). Where a claim could not be verified against a primary source, it is flagged explicitly in the text.

The Anatomy of a Root Cause Engine: Correlating Hidden Patterns in Distributed Telemetry

Information gain is a very important concept in information theory. In the most simplest case, it is the reduction in entropy. If you are not familiar with entropy, check out my entropy post. Information gain is used in decision trees. A decision tree is a classification algorithm. Decision tree splits the given feature from specific points with specific questions by branching. By doing that, it tries to maximize information gain. In other words, it tries to minimize the entropy in each branch. To sum up, information gain tells us the reduction in entropy in each split.

Entropy is one of the key concepts of information theory, data science and machine learning. Understanding the math behind it is crucial for designing solid machine learning pipelines. Therefore, in this post, I try to explain the entropy as simple as possible.