Operations | Monitoring | ITSM | DevOps | Cloud

Summary: Machine learning-based anomaly detection improves observability by learning normal system behavior instead of relying only on static thresholds. This article explains how vmanomaly, its MCP server, purpose-built skills, and an LLM-powered UI copilot help engineers explore telemetry, investigate anomalies, build MetricsQL queries, select suitable models, apply business constraints, and validate configurations through natural language.

Summary: The Q2 2026 development cycle moved VictoriaMetrics Anomaly Detection toward one simpler, continuously adapting workflow. The main addition is Temporal Envelope, an online model that handles trend, multiple calendar patterns, holidays, persistent changes, forecasts, and optional multivariate context without retaining the full fit history.