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* Deployment Guides Improved * Windows installer + ML (all) improved * fix formatting and some minor changes --------- Co-authored-by: Ilya Mashchenko <ilya@netdata.cloud>
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19 lines
No EOL
1.5 KiB
Markdown
# Machine Learning and Anomaly Detection
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Netdata includes advanced Machine Learning capabilities to help you detect and resolve anomalies in your infrastructure before they escalate into critical issues. These features provide real-time insights and proactive monitoring to improve system reliability.
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## Key Features
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### Anomaly Detection with K-Means Clustering
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Netdata trains K-means clustering models to detect anomalies in your infrastructure. These models power the [Anomaly Advisor](/docs/dashboards-and-charts/anomaly-advisor-tab.md), which visually highlights anomalies on the dashboard, allowing you to quickly identify and investigate unexpected behavior.
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### Metric Correlations
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Netdata enables metric correlation analysis through the dashboard. This feature uses the [Two-sample Kolmogorov-Smirnov test](https://en.wikipedia.org/wiki/Kolmogorov%E2%80%93Smirnov_test#Two-sample_Kolmogorov%E2%80%93Smirnov_test) and volume heuristic measures to help you understand relationships between different metrics and identify potential causes of anomalies.
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### Netdata Assistant for Troubleshooting
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The [Netdata Assistant](/docs/netdata-assistant.md) provides AI-driven assistance for troubleshooting alerts and anomalies. You can interact with it directly to get explanations, recommendations, and next steps based on detected anomalies and system behavior.
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These Machine Learning features enhance observability and streamline incident response, helping you maintain system health with greater efficiency. |