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47 lines
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47 lines
4.0 KiB
Markdown
In the dynamic and complex world of system monitoring, VictoriaMetrics Anomaly Detection, being a part of our [Enterprise offering](https://victoriametrics.com/products/enterprise/), stands as a pivotal tool for achieving advanced observability. It empowers SREs and DevOps teams by automating the intricate task of identifying abnormal behavior in time-series data. It goes beyond traditional threshold-based alerting, utilizing machine learning techniques to not only detect anomalies but also minimize false positives, thus reducing alert fatigue. By providing simplified alerting mechanisms atop of [unified anomaly scores](./components/models.md#vmanomaly-output), it enables teams to spot and address potential issues faster, ensuring system reliability and operational efficiency.
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## Practical Guides and Installation
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Begin your VictoriaMetrics Anomaly Detection journey with ease using our guides and installation instructions:
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- **Quickstart**: Check out how to get `vmanomaly` up and running [here](./QuickStart.md).
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- **Overview**: Find out how `vmanomaly` service operates [here](./Overview.md)
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- **Integration**: Integrate anomaly detection into your observability ecosystem. Get started [here](./guides/guide-vmanomaly-vmalert/README.md).
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- **Anomaly Detection Presets**: Enable anomaly detection on predefined set of indicators, that require frequently changing static thresholds for alerting. Find more information [here](./Presets.md).
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- **Installation Options**: Select the method that aligns with your technical requirements:
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- **Docker Installation**: Suitable for containerized environments. See [Docker guide](./Overview.md#run-vmanomaly-docker-container).
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- **Helm Chart Installation**: Appropriate for those using Kubernetes. See our [Helm charts](https://github.com/VictoriaMetrics/helm-charts/tree/master/charts/victoria-metrics-anomaly).
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> **Note**: starting from [v1.5.0](./CHANGELOG.md#v150) `vmanomaly` requires a [license key](./Overview.md#licensing) to run. You can obtain a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/).
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## Key Components
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Explore the integral components that configure VictoriaMetrics Anomaly Detection:
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* [Explore components and their interation](./components/README.md)
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- [Models](./components/models.md)
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- [Reader](./components/reader.md)
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- [Scheduler](./components/scheduler.md)
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- [Writer](./components/writer.md)
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- [Monitoring](./components/monitoring.md)
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## Deep Dive into Anomaly Detection
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Enhance your knowledge with our handbook on Anomaly Detection & Root Cause Analysis and stay updated:
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* Anomaly Detection Handbook
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- [Introduction to Time Series Anomaly Detection](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/)
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- [Types of Anomalies in Time Series Data](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-2/)
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- [Techniques and Models for Anomaly Detection](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-3/)
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* Follow the [`#anomaly-detection`](https://victoriametrics.com/blog/tags/anomaly-detection/) tag in our blog
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## Frequently Asked Questions (FAQ)
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Got questions about VictoriaMetrics Anomaly Detection? Chances are, we've got the answers ready for you.
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Dive into [our FAQ section](./FAQ.md) to find responses to common questions.
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## Get in Touch
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We are eager to connect with you and adapt our solutions to your specific needs. Here's how you can engage with us:
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* [Book a Demo](https://calendly.com/victoriametrics-anomaly-detection) to discover what our product can do.
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* Interested in exploring our [Enterprise features](https://victoriametrics.com/products/enterprise), including [Anomaly Detection](https://victoriametrics.com/products/enterprise/anomaly-detection)? [Request your trial license](https://victoriametrics.com/products/enterprise/trial/) today and take the first step towards advanced system observability.
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---
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Our [CHANGELOG is just a click away](./CHANGELOG.md), keeping you informed about the latest updates and enhancements.
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