---
title: "What is AIOps?"
description: "AIOps applies AI and machine learning to IT operations to analyze logs, metrics, traces, events, alerts, and related system data. It detects anomalies, correlates events, uses topology and dependencies to identify likely root causes, and supports guided or automated responses. The guide positions AIOps between observability and action, reducing noise and adding context without replacing observability, DevOps, or human judgment. Its workflow includes data ingestion, normalization, anomaly detection, event correlation, root-cause analysis, automation and orchestration, and collaboration workflows, with human-in-the-loop review recommended for higher-risk production changes. It contrasts domain-centric and domain-agnostic approaches and recommends evaluating coverage, depth, integrations, setup effort, and organizational fit before expanding automation."
---

# What is AIOps?

[Databricks](https://yomu.fyi/company/databricks) · Databricks Staff · Sep 17, 2026

**Type:** Explainer

## Summary

AIOps applies AI and machine learning to IT operations to analyze logs, metrics, traces, events, alerts, and related system data. It detects anomalies, correlates events, uses topology and dependencies to identify likely root causes, and supports guided or automated responses. The guide positions AIOps between observability and action, reducing noise and adding context without replacing observability, DevOps, or human judgment. Its workflow includes data ingestion, normalization, anomaly detection, event correlation, root-cause analysis, automation and orchestration, and collaboration workflows, with human-in-the-loop review recommended for higher-risk production changes. It contrasts domain-centric and domain-agnostic approaches and recommends evaluating coverage, depth, integrations, setup effort, and organizational fit before expanding automation.

## Context

AIOps is presented as a response to growing operational-data volume and infrastructure complexity across microservices, multi-cloud dependencies, AI workloads, and agent-driven systems. It is described as complementary to observability and DevOps, helping engineers prioritize signals, investigate incidents, and respond faster while retaining human oversight for higher-risk actions.

## Approach / What changed

The explicit approach is to collect and consolidate operational signals, clean and normalize them, detect deviations from learned behavioral baselines, correlate related events, analyze topology and dependencies for likely causes, and then recommend or execute predefined responses. Teams are advised to begin with recommendations, measure metrics such as alert volume and MTTR, and expand automation carefully.

## Takeaways

- AIOps learns normal behavior across time periods instead of relying only on static thresholds, then alerts when observed behavior deviates from the baseline.
- Event correlation groups related alerts, while topology-aware analysis distinguishes a likely degraded root service from downstream symptoms.
- Higher-risk production actions should use human-in-the-loop review; lower-risk predefined responses can be recommended or automated.

## FAQ

### How does AIOps differ from observability?

Observability collects and helps explain what is happening across systems. AIOps builds on those signals by applying AI and machine learning to detect anomalies, correlate events, identify likely causes, and support or automate responses.

### What operational data can an AIOps platform analyze?

AIOps can analyze logs, metrics, traces, events, alerts, configuration data, and other signals from IT systems. Platforms may consolidate these sources into a data lake, data warehouse, or lakehouse.

### When should AIOps automate a response?

AIOps can recommend or execute predefined actions such as restarting a service, scaling infrastructure, rolling back a deployment, or opening a ticket. Higher-risk actions should require engineers to review and approve the response before it affects production.

**Tags:** [AIOps](https://yomu.fyi/topic/aiops), [Automation](https://yomu.fyi/topic/automation), [Incident Response](https://yomu.fyi/topic/incident-response), [Machine Learning](https://yomu.fyi/topic/machine-learning), [Observability](https://yomu.fyi/topic/observability)

- Source: [Databricks](https://www.databricks.com/blog/what-is-aiops)
- Source URL: https://www.databricks.com/blog/what-is-aiops
- Ingested by Yomu: 2026-09-17T21:00:06.800Z

[Read original post](https://www.databricks.com/blog/what-is-aiops)