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docs: vmanomaly chapter hotfixes (#5583)
* update link to book a demo for AD & RCA * fix invalid refs in components/model * - fix staging -> prod links - replace capitalized FAQ headers - change the section order on main page - replace :latest tag with current stable
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@ -18,36 +18,36 @@ VictoriaMetrics Anomaly Detection, also known as `vmanomaly`, is a service for d
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Please refer to [our guide section](/anomaly-detection/#practical-guides-and-installation) to find out more.
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## How Does vmanomaly Work?
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## How does vmanomaly work?
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`vmanomaly` applies built-in (or custom) [anomaly detection algorithms](/anomaly-detection/components/models), specified in a config file. Although a single config file supports one model, running multiple instances of `vmanomaly` with different configs is possible and encouraged for parallel processing or better support for your use case (i.e. simpler model for simple metrics, more sophisticated one for metrics with trends and seasonalities).
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Please refer to [about](/vmanomaly.html#about) section to find out more.
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## What Data Does vmanomaly Operate On?
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## What data does vmanomaly operate on?
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`vmanomaly` operates on data fetched from VictoriaMetrics, where you can leverage full power of [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) for data selection, sampling, and processing. Users can also [apply global filters](https://docs.victoriametrics.com/#prometheus-querying-api-enhancements) for more targeted data analysis, enhancing scope limitation and tenant visibility.
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Respective config is defined in a [`reader`](/anomaly-detection/components/reader.html#vm-reader) section.
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## Handling Noisy Input Data
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## Handling noisy input data
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`vmanomaly` operates on data fetched from VictoriaMetrics using [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) queries, so the initial data quality can be fine-tuned with aggregation, grouping, and filtering to reduce noise and improve anomaly detection accuracy.
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## Output Produced by vmanomaly
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## Output produced by vmanomaly
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`vmanomaly` models generate [metrics](/anomaly-detection/components/models/models.html#vmanomaly-output) like `anomaly_score`, `yhat`, `yhat_lower`, `yhat_upper`, and `y`. These metrics provide a comprehensive view of the detected anomalies. The service also produces [health check metrics](/anomaly-detection/components/monitoring.html#metrics-generated-by-vmanomaly) for monitoring its performance.
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## Choosing the Right Model for vmanomaly
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## Choosing the right model for vmanomaly
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Selecting the best model for `vmanomaly` depends on the data's nature and the types of anomalies to detect. For instance, [Z-score](anomaly-detection/components/models/models.html#z-score) is suitable for data without trends or seasonality, while more complex patterns might require models like [Prophet](anomaly-detection/components/models/models.html#prophet).
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Please refer to [respective blogpost on anomaly types and alerting heuristics](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-2/) for more details.
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Still not 100% sure what to use? We are [here to help](/anomaly-detection/#get-in-touch).
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## Alert Generation in vmanomaly
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## Alert generation in vmanomaly
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While `vmanomaly` detects anomalies and produces scores, it *does not directly generate alerts*. The anomaly scores are written back to VictoriaMetrics, where an external alerting tool, like [`vmalert`](/vmalert.html), can be used to create alerts based on these scores for integrating it with your alerting management system.
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## Preventing Alert Fatigue
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## Preventing alert fatigue
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Produced anomaly scores are designed in such a way that values from 0.0 to 1.0 indicate non-anomalous data, while a value greater than 1.0 is generally classified as an anomaly. However, there are no perfect models for anomaly detection, that's why reasonable defaults expressions like `anomaly_score > 1` may not work 100% of the time. However, anomaly scores, produced by `vmanomaly` are written back as metrics to VictoriaMetrics, where tools like [`vmalert`](/vmalert.html) can use [MetricsQL](https://docs.victoriametrics.com/MetricsQL.html) expressions to fine-tune alerting thresholds and conditions, balancing between avoiding [false negatives](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#false-negative) and reducing [false positives](https://victoriametrics.com/blog/victoriametrics-anomaly-detection-handbook-chapter-1/#false-positive).
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## Resource Consumption of vmanomaly
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## Resource consumption of vmanomaly
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`vmanomaly` itself is a lightweight service, resource usage is primarily dependent on [scheduling](/anomaly-detection/components/scheduler.html) (how often and on what data to fit/infer your models), [# and size of timeseries returned by your queries](/anomaly-detection/components/reader.html#vm-reader), and the complexity of the employed [models](anomaly-detection/components/models). Its resource usage is directly related to these factors, making it adaptable to various operational scales.
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## Scaling vmanomaly
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@ -18,6 +18,18 @@ aliases:
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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](/anomaly-detection/components/models/models.html#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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- **Quick Start**: Find out what is behind `vmanomaly` [here](/vmanomaly.html)
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- **Integration**: Simplify the process of integrating anomaly detection into your observability ecosystem. Get started [**here**](/anomaly-detection/guides/guide-vmanomaly-vmalert.html).
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- **Installation Options**: Choose the method that best fits your environment:
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- **Docker Installation**: Ideal for containerized environments. Follow our [Docker guide](../vmanomaly.md#run-vmanomaly-docker-container) for a smooth setup.
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- **Helm Chart Installation**: Perfect for Kubernetes users. Deploy using our [Helm charts](https://github.com/VictoriaMetrics/helm-charts/tree/master/charts/victoria-metrics-anomaly) for an efficient integration.
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> Note: starting from [v1.5.0](./CHANGELOG.md#v150) `vmanomaly` requires a [license key](/vmanomaly.html#licensing) to run. You can obtain a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/index.html).
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## Key Components
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Explore the integral components that configure VictoriaMetrics Anomaly Detection:
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* [Get familiar with components](/anomaly-detection/components)
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@ -27,17 +39,6 @@ Explore the integral components that configure VictoriaMetrics Anomaly Detection
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- [Writer](/anomaly-detection/components/writer.html)
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- [Monitoring](/anomaly-detection/components/monitoring.html)
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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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- **Quick Start Guide**: Jumpstart your anomaly detection setup to simplify the process of integrating anomaly detection into your observability ecosystem. Get started [**here**](/anomaly-detection/guides/guide-vmanomaly-vmalert.html).
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- **Installation Options**: Choose the method that best fits your environment:
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- **Docker Installation**: Ideal for containerized environments. Follow our [Docker guide](../vmanomaly.md#run-vmanomaly-docker-container) for a smooth setup.
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- **Helm Chart Installation**: Perfect for Kubernetes users. Deploy using our [Helm charts](https://github.com/VictoriaMetrics/helm-charts/tree/master/charts/victoria-metrics-anomaly) for an efficient integration.
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> Note: starting from [v1.5.0](./CHANGELOG.md#v150) `vmanomaly` requires a [license key](/vmanomaly.html#licensing) to run. You can obtain a trial license key [**here**](https://victoriametrics.com/products/enterprise/trial/index.html).
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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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@ -53,8 +54,8 @@ Dive into [our FAQ section](/anomaly-detection/FAQ.html) to find responses to co
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## Get in Touch
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We're eager to connect with you and tailor our solutions to your specific needs. Here's how you can engage with us:
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* [Book a Demo](https://calendly.com/fred-navruzov/) to discover what our product can do.
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* Interested in exploring our [Enterprise features](https://new.victoriametrics.com/products/enterprise), including Anomaly Detection? [Request your trial license](https://new.victoriametrics.com/products/enterprise/trial/) today and take the first step towards advanced system observability.
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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? [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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@ -17,4 +17,4 @@ aliases:
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This section describes `Model` component of VictoriaMetrics Anomaly Detection (or simply [`vmanomaly`](/vmanomaly.html)) and the guide of how to define respective section of a config to launch the service.
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Please find a guide of how to use [built-in models](/anomaly-detection/docs/models/models.html) for anomaly detection, as well as how to define and use your own [custom model](/anomaly-detection/docs/models/custom_model.html).
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Please find a guide of how to use [built-in models](/anomaly-detection/components/models/models.html) for anomaly detection, as well as how to define and use your own [custom model](/anomaly-detection/components/models/custom_model.html).
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@ -147,7 +147,7 @@ There are 4 required sections in config file:
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[`monitoring`](#monitoring) - defines how to monitor work of *vmanomaly* service. This config section is *optional*.
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> For a detailed description, see [config sections](/anomaly-detection/docs)
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> For a detailed description, see [config sections](/anomaly-detection/components)
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#### Config example
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Here is an example of config file that will run FB Prophet model, that will be retrained every 2 hours on 14 days of previous data. It will generate inference (including `anomaly_score` metric) every 1 minute.
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@ -216,9 +216,11 @@ This will expose metrics at `http://0.0.0.0:8080/metrics` page.
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To use *vmanomaly* you need to pull docker image:
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```sh
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docker pull us-docker.pkg.dev/victoriametrics-test/public/vmanomaly-trial:latest
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docker pull us-docker.pkg.dev/victoriametrics-test/public/vmanomaly-trial:1.7.2
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```
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> Note: please check what is latest release in [CHANGELOG](/anomaly-detection/CHANGELOG.html)
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You can put a tag on it for your convinience:
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```sh
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