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authorAndrew Maguire <andrewm4894@gmail.com>2022-07-21 19:47:56 +0100
committerGitHub <noreply@github.com>2022-07-21 19:47:56 +0100
commitc60e10347825072aa77b5bcfa4aa1942fc564920 (patch)
tree9b7fde7c06cadc8df3a0f9a5f0605b09097128af /README.md
parentfccfc02d2c1966b814b6babcd8dddb0c6e822d45 (diff)
add ML bullet point to features section on README (#13418)
add ML bullet point to features section on README
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@@ -72,6 +72,7 @@ Here's what you can expect from Netdata:
synchronize charts as you pan through time, zoom in on anomalies, and more.
- **Visual anomaly detection**: Our UI/UX emphasizes the relationships between charts to help you detect the root
cause of anomalies.
+- **Machine learning (ML) features out of the box**: Unsupervised ML-based [anomaly detection](https://learn.netdata.cloud/docs/cloud/insights/anomaly-advisor), every second, every metric, zero-config! [Metric correlations](https://learn.netdata.cloud/docs/cloud/insights/metric-correlations) to help with short-term change detection. And other [additional](https://learn.netdata.cloud/guides/monitor/anomaly-detection) ML-based features to help make your life easier.
- **Scales to infinity**: You can install it on all your servers, containers, VMs, and IoT devices. Metrics are not
centralized by default, so there is no limit.
- **Several operating modes**: Autonomous host monitoring (the default), headless data collector, forwarding proxy,