---
title: "Tackle trends and anomalies with machine learning | Grafana Labs"
description: "Machine learning features that predict where a metric is heading and flag group members behaving differently from their peers."
---

> For a curated documentation index, see [llms.txt](/llms.txt). For the complete documentation index, see [llms-full.txt](/llms-full.txt).

## Get ahead of problems before users notice

Fixed thresholds have real limitations.

[A metric rising for hours with no signal, then an alert fires only after the limit is crossed](threshold-limitation.svg "A metric rising for hours with no signal, then an alert fires only after the limit is crossed")

| Limitation                                                           | How it affects you                                      |
|----------------------------------------------------------------------|---------------------------------------------------------|
| Reactive by design: Alert fires only after the threshold is crossed. | Reacting to capacity issues instead of preventing them  |
| Can’t express how one instance differs from the rest                 | Outliers hide in aggregate data                         |
| No trend projection                                                  | Guessing at capacity planning instead of forecasting it |

With Grafana Cloud Machine Learning, you can catch what threshold alerting can’t:

- An instance trending toward trouble
- A single node quietly diverging from the rest of the cluster

Grafana Cloud machine learning offers **forecasting** and **outlier detection**.

## Real results from real teams

- [**MediaKind**](/blog/introducing-grafana-machine-learning-for-grafana-cloud-with-metrics-forecasting/)
  
  > “We see (Grafana Machine Learning) as a very useful tool for intelligent anomaly detection, and it will certainly become one of the tools that our SREs will use to increase their productivity and reduce their daily toil.”