Anomaly-Detection on Netdata

Anomaly-Detection on Netdatahttps://www.netdata.cloud/tags/anomaly-detection/Recent content in Anomaly-Detection on NetdataHugoen-usThu, 25 Jun 2026 00:00:00 +0000Robot Fleet Monitoring with Per-Second Edge Intelligencehttps://www.netdata.cloud/solutions/industries/robotics/Thu, 25 Jun 2026 00:00:00 +0000https://www.netdata.cloud/solutions/industries/robotics/Netdata runs on each robot, collecting per-second metrics for compute, memory, storage, thermals, and network — keeping thousands of devices observable even on intermittent site networks, without centralized SaaS.How To Reduce Alert Fatigue With Anomaly Detectionhttps://www.netdata.cloud/solutions/use-cases/alert-fatigue/Sat, 30 May 2026 00:00:00 +0000https://www.netdata.cloud/solutions/use-cases/alert-fatigue/Most alert-fatigue tools manage noise after the fact. Netdata prevents it by running 18 ML models per metric with consensus voting — anomalies only fire when multiple models agree, suppressing the false positives that drive on-call burnout.AI Co-Engineer For Instant Root Cause Insightshttps://www.netdata.cloud/features/aiml/ai-co-engineer/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/features/aiml/ai-co-engineer/Netdata’s AI Co-Engineer combines edge-native machine learning with flexible AI integration, providing instant expert-level insights while keeping your data sovereign and secure.AIOps Platform: Edge-Native ML, Zero Configurationhttps://www.netdata.cloud/features/aiml/aiops/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/features/aiml/aiops/Enterprise AIOps intelligence without enterprise complexity. Edge-native ML, automated insights, and transparent pricing deliver operational excellence from day one.Anomaly Detection Software: 99% Fewer False Positiveshttps://www.netdata.cloud/features/aiml/anomaly-detection/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/features/aiml/anomaly-detection/Production-ready ML anomaly detection from installation. 18 consensus models per metric achieve 99% false positive reduction in anomaly detection while catching issues competitors miss. Zero configuration. Zero false promises.Anomaly Detection With 99% Fewer False Positiveshttps://www.netdata.cloud/features/aiml/machine-learning/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/features/aiml/machine-learning/Netdata trains 18 independent ML models per metric at the edge, achieving 99% false positive reduction in anomaly detection through unanimous consensus - all included at no additional cost.Blast Radius Detection For Faster Incident Responsehttps://www.netdata.cloud/features/aiml/blast-radius-detection/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/features/aiml/blast-radius-detection/Netdata reveals blast radius dynamically through real-time anomaly correlation and ML-powered pattern recognition, showing the complete story from first failure to full impact in seconds.Database Monitoring Software With Real-Time Visibilityhttps://www.netdata.cloud/solutions/use-cases/database-monitoring/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/solutions/use-cases/database-monitoring/Real-time database monitoring with AI-powered troubleshooting, zero configuration, and predictable costs. Monitor 15+ database platforms with per-second granularity and ML-based anomaly detection.IoT Monitoring Solution With Full Data Sovereigntyhttps://www.netdata.cloud/solutions/use-cases/iot-monitoring/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/solutions/use-cases/iot-monitoring/Real-time IoT monitoring with per-device metrics, ML-powered anomaly detection, and transparent pricing. Monitor thousands of sensors, gateways, and edge devices with <2s latency.Native iOS & Android Apps With AI Troubleshootinghttps://www.netdata.cloud/product/mobile-apps/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/product/mobile-apps/Transform on-call monitoring with Netdata’s mobile apps for iOS and Android. Get push notifications for infrastructure alerts, AI-powered root cause analysis, and real-time dashboard access - all at a fixed, predictable price that includes mobile access for your entire team.Netdata vs N-able: Real-Time Monitoring Comparisonhttps://www.netdata.cloud/comparisons/nable/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/comparisons/nable/Netdata provides per-second infrastructure monitoring with ML-based anomaly detection and AI troubleshooting - capabilities N-able’s 5-10 minute intervals and basic dashboards can’t match. See how Netdata solves the monitoring gaps N-able customers experience daily.Netdata vs Zabbix | Monitoring Tools Comparisonhttps://www.netdata.cloud/comparisons/zabbix/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/comparisons/zabbix/Real-Time Infrastructure Observability For CISOshttps://www.netdata.cloud/solutions/built-for/cisos/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/solutions/built-for/cisos/Transform infrastructure security monitoring with real-time visibility, edge-based ML anomaly detection, and zero-configuration deployment. Netdata empowers CISOs to detect threats faster, reduce operational burden, and maintain data sovereignty - all at 90% lower cost than traditional SIEMs.Real-Time Infrastructure Troubleshooting With AIhttps://www.netdata.cloud/solutions/use-cases/troubleshooting/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/solutions/use-cases/troubleshooting/Transform troubleshooting from hours to minutes with Netdata’s real-time observability platform. Get per-second metrics, automatic anomaly detection, and AI-guided investigations—all with zero configuration and 90% cost savings.Real-Time Observability For SRE Teamshttps://www.netdata.cloud/solutions/built-for/sre/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/solutions/built-for/sre/Transform SRE operations with Netdata’s edge-native observability platform. Get per-second visibility, ML anomaly detection on every metric, and AI-powered troubleshooting at 90% lower cost than traditional solutions.Real-Time Troubleshooting With Sub-2-Second Latencyhttps://www.netdata.cloud/features/visualization/troubleshooting/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/features/visualization/troubleshooting/Interactive debugging with per-second precision and full historical context. Netdata transforms troubleshooting through edge-native ML, automated correlation, and AI-powered analysis.Root Cause Analysis (RCA) With 80% MTTR Reductionhttps://www.netdata.cloud/features/aiml/root-cause-analysis/Thu, 18 Dec 2025 00:00:00 +0000https://www.netdata.cloud/features/aiml/root-cause-analysis/Netdata’s edge-native ML detects anomalies as they happen, automated correlation surfaces root causes in the top 30-50 results, and AI explains incidents in plain English - achieving 80% MTTR reduction at 90% lower cost.Our first ML based anomaly alerthttps://www.netdata.cloud/blog/our-first-ml-based-anomaly-alert/Wed, 13 Sep 2023 00:00:00 +0000https://www.netdata.cloud/blog/our-first-ml-based-anomaly-alert/<p>Over the last few years we have slowly and methodically been building out the <a href="https://learn.netdata.cloud/docs/ml-and-troubleshooting/">ML based capabilities</a> of the Netdata agent, dogfooding and iterating as we go. To date, these features have mostly been somewhat reactive and tools to aid once you are already troubleshooting.</p> <p>Now we feel we are ready to take a first gentle step into some more proactive use cases, starting with a <a href="https://github.com/netdata/netdata/pull/14687">simple node level anomaly rate alert</a>.</p>Anomaly Rate By Typehttps://www.netdata.cloud/blog/anomaly-rate-by-type/Wed, 30 Aug 2023 00:00:00 +0000https://www.netdata.cloud/blog/anomaly-rate-by-type/<p>We have <a href="https://github.com/netdata/netdata/pull/15856">recently added</a> a more detailed anomaly rate chart to Netdata that breaks out the overall <a href="https://learn.netdata.cloud/docs/ml-and-troubleshooting/machine-learning-ml-powered-anomaly-detection#node-anomaly-rate">node anomaly rate</a> by type, this lets you more easily see what parts of your infrastructure might be experiencing an uptick in anomalies when you see the overall node anomaly rate increase.</p> <h2 id="what-is-type">What is <code>type</code>?</h2> <p><code>type</code> is generally the prefix of the chart id in Netdata and controls where charts live within the menu on the overview page, for example the <code>mem.available</code> chart has a type of <code>mem</code> which in part controls why it lives under the “Memory” section of the menu.</p>How Netdata's ML-based Anomaly Detection Workshttps://www.netdata.cloud/blog/how-netdatas-ml-based-anomaly-detection-works/Tue, 23 May 2023 00:00:00 +0000https://www.netdata.cloud/blog/how-netdatas-ml-based-anomaly-detection-works/<p><img src="../2023-05-23-how-netdatas-ml-based-anomaly-detection-works/img/img.png" alt="title image"></p> <p>How does Netdata’s <a href="https://learn.netdata.cloud/docs/troubleshooting-and-machine-learning/machine-learning-ml-powered-anomaly-detection">machine learning (ML) based anomaly detection</a> actually work? Read on to find out!</p>  <h2 id="design-considerations">Design considerations</h2> <p>Lets first start with some of the key design considerations and principles of Netdata’s anomaly detection (<em>and some comments in parenthesis along the way</em>):</p> <ol> <li>We don’t have any labels or examples of previous anomalies. This means we are in an <a href="https://en.wikipedia.org/wiki/Unsupervised_learning">unsupervised setting</a> (<em>best we can try to do is learn what “normal” data looks like assuming the collected data is “mostly” normal</em>).</li> <li>Needs to be lightweight and run on the agent (<em>or a parent</em>). <ul> <li>Need to be very careful of impact on CPU overhead when training and scoring (<em>lots of cheap models are better than a few expensive and heavy ones</em>).</li> <li>Models themselves need to be small so as to not drastically increase the agents memory footprint (<em>model objects need to be small for storage</em>).</li> <li>This has implications for the ML formulation (<em>sorry - no deep learning models yet) and its implementation (we need to be surgical and optimized</em>).</li> </ul> </li> <li>Needs to scale for thousands of metrics and score in realtime every second as metrics are collected. <ul> <li>Typical Netdata nodes have thousands of metrics and we want to be able to score every metric every second with minimal latency overhead (<em>we need to use sensible approaches to training like spreading the training cost over a wide training window</em>).</li> </ul> </li> <li>Needs to be able to handle a wide variety of metrics. <ul> <li>There is no single perfect model or approach for all types of metrics so we need a good all rounder that can work well enough across any and all different types of time seres metrics (<em>for any given metric of course you could handcraft a better model but thats not feasible here, we need something like a “weak learners” approach of lots of generally useful models adding up to “more than the sum of their parts”</em>).</li> </ul> </li> <li>Needs to be written in C or C++ as that is the language of the Netdata agent (<em>We are using <a href="https://github.com/davisking/dlib">dlib</a> for the current implementation</em>).</li> <li>We Need to be very careful about taking big or complex dependencies if using third party libraries. <ul> <li>We want to be able to easily build and deploy Netdata on any Linux system without having to worry about installing or managing complex dependencies (<em>we need to be careful of more complex algorithms that would have larger dependencies and potentially limit where Netdata can run</em>).</li> </ul> </li> </ol> <p>The above considerations are important and useful to keep in mind as we explore the system in more detail.</p>Anomaly Rates in the Menu!https://www.netdata.cloud/blog/anomaly-rates-in-the-menu/Wed, 29 Mar 2023 00:00:00 +0000https://www.netdata.cloud/blog/anomaly-rates-in-the-menu/<p>The menu (on the <a href="https://learn.netdata.cloud/docs/getting-started/monitor-your-infrastructure/home-overview-and-single-node-view#overview-and-single-node-view">overview or single node tab</a>) now has an <a href="https://learn.netdata.cloud/docs/troubleshooting-and-machine-learning/machine-learning-ml-powered-anomaly-detection#anomaly-rate">anomaly rate</a> button built into it that, for the entire visible window or a highlighted time range, shows the maximum chart anomaly rate within each section.</p> <p>Read on to learn more about this new feature!</p> <iframe width="560" height="315" src="https://www.youtube.com/embed/PgVh_MFHMb0?si=F2Mq6wIxHJWaHykZ" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> <h2 id="wait-what-is-an-anomaly-rate">Wait, what is an anomaly rate?</h2> <p>Netdata is the only monitoring agent that natively (for every metric, with zero config and sane defaults) produces anomaly rates in addition to just collecting raw metrics.</p>Anomaly detection on Prometheus metricshttps://www.netdata.cloud/blog/anomaly-detection-on-prometheus-metrics/Wed, 01 Mar 2023 00:00:00 +0000https://www.netdata.cloud/blog/anomaly-detection-on-prometheus-metrics/<p><img src="../2023-03-01-anomaly-detection-on-prometheus-metrics/img/img.png" alt="img"></p> <p>We have recently extended the native machine learning (ML) based anomaly detection <a href="https://learn.netdata.cloud/guides/monitor/anomaly-detection">capabilities</a> of Netdata to <a href="https://github.com/netdata/netdata/issues/14218">support all metrics</a>, regardless on their collection frequency (<code>update every</code>).</p> <p>Previously only metrics collected every second were supported, but now Netdata can run anomaly detection out of the box with zero config on metrics with any collection frequency.</p> <p>This post will illustrate an example of what this means using <a href="https://prometheus.io/">Prometheus</a> metrics (via the <a href="https://learn.netdata.cloud/docs/agent/collectors/go.d.plugin/modules/prometheus#gsc.tab=0">Netdata Prometheus collector</a>) since they typically have a default collection frequency of 10 seconds.</p>Extending Netdata's anomaly detection training windowhttps://www.netdata.cloud/blog/extending-anomaly-detection-training-window/Thu, 02 Feb 2023 00:00:00 +0000https://www.netdata.cloud/blog/extending-anomaly-detection-training-window/<p>We have been busy at work under the hood of the Netdata agent to introduce new capabilities that let you extend the “training window” used by Netdata’s <a href="https://learn.netdata.cloud/docs/nightly/setup/configure-machine-learning-ml-powered-anomaly-detection">native anomaly detection capabilities</a>.</p> <p>This blog post will discuss one of these improvements to help you reduce “<a href="https://en.wikipedia.org/wiki/False_positives_and_false_negatives#False_positive_error">false positives</a>” by essentially extending the training window by using the new (beautifully named) <code>number of models per dimension</code> configuration parameter.</p> <h2 id="background">Background</h2> <p>One of the most important considerations of our native anomaly detection capabilities is the overhead of running the training and scoring computations required to train thousands of models (one per metric) and produce <a href="https://learn.netdata.cloud/docs/nightly/setup/configure-machine-learning-ml-powered-anomaly-detection#anomaly-bit">anomaly bits</a> every second based on those trained models.</p>How Netdata’s Machine Learning workshttps://www.netdata.cloud/blog/how-netdatas-machine-learning-works/Thu, 01 Sep 2022 00:00:00 +0000https://www.netdata.cloud/blog/how-netdatas-machine-learning-works/<p>Following on from the <a href="https://www.netdata.cloud/blog/introducing-anomaly-advisor-unsupervised-anomaly-detection-in-netdata" target="_blank" rel="noopener">recent launch</a> of our <a href="https://learn.netdata.cloud/docs/cloud/insights/anomaly-advisor" target="_blank" rel="noopener">Anomaly Advisor</a> feature, and in keeping with <a href="https://www.netdata.cloud/blog/our-approach-to-machine-learning/" target="_blank" rel="noopener">our approach to machine learning</a>, <a href="https://github.com/netdata/netdata/blob/master/ml/notebooks/netdata_anomaly_detection_deepdive.ipynb" target="_blank" rel="noopener">here</a> is a detailed Python notebook outlining exactly how the machine learning powering the Anomaly Advisor actually works under the hood.</p>  <p>Or if you’d rather watch a video walkthrough of the notebook then check out below.</p> <iframe width="560" height="315" src="https://www.youtube.com/embed/L1xleckyuDQ?si=rptYzWE-eLlhSL9x" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe> <p>Try it for yourself, <a href="https://learn.netdata.cloud/docs/cloud/get-started" target="_blank" rel="noopener">get started</a> by <a href="https://app.netdata.cloud/?utm_source=blog&utm_content=how_netdata_ml_works" target="_blank" rel="noopener">signing in to Netdata</a> and connecting a node. Once initial models have been trained (usually after the agent has about one hour of data, zero configuration needed), you’ll be able to start exploring in the <a href="https://learn.netdata.cloud/docs/cloud/insights/anomaly-advisor" target="_blank" rel="noopener">Anomaly Advisor</a> tab of Netdata.</p>Anomaly rate in every charthttps://www.netdata.cloud/blog/anomaly-rate-in-every-chart/Thu, 23 Jun 2022 00:00:00 +0000https://www.netdata.cloud/blog/anomaly-rate-in-every-chart/<p>A month ago, we introduced unsupervised ML & Anomaly Detection in Netdata, the <a href="https://www.netdata.cloud/blog/introducing-anomaly-advisor-unsupervised-anomaly-detection-in-netdata/">Anomaly Advisor</a>. Today, we’re happy to announce that we’re bringing anomaly rates to every chart in Netdata Cloud. Anomaly information is no longer limited to the Anomalies tab and will be accessible to you from the Overview and Single Node View tabs as well. This will make your troubleshooting journey easier, as you will have the anomaly rates for any metric available with a single click. Whichever metric or chart you’re exploring will be instant.</p>Anomaly Advisor: Unsupervised Anomaly Detectionhttps://www.netdata.cloud/blog/introducing-anomaly-advisor-unsupervised-anomaly-detection-in-netdata/Thu, 26 May 2022 00:00:00 +0000https://www.netdata.cloud/blog/introducing-anomaly-advisor-unsupervised-anomaly-detection-in-netdata/<p>Today we are excited to launch one of our flagship ML assisted troubleshooting features in Netdata – the Anomaly Advisor.</p> <p>The Anomaly Advisor builds on earlier work to introduce unsupervised <a href="https://github.com/netdata/netdata/blob/master/ml/README.md">anomaly detection</a> capabilities into the <a href="https://www.netdata.cloud/agent/">Netdata Agent</a> from <a href="https://github.com/netdata/netdata/releases/tag/v1.32.0">v1.32.0</a> onwards.</p> <h2 id="getting-started">Getting Started</h2> <p>Once you <a href="https://learn.netdata.cloud/docs/configure/machine-learning#configuration">enable ML</a> on your nodes, each node will begin producing an “<a href="https://learn.netdata.cloud/docs/configure/machine-learning#anomaly-bit---100--anomalous-0--normal">Anomaly Bit</a>” every second in addition to raw metric values. This anomaly bit will be 1 when the trained ML models consider recent raw data for a metric to look anomalous or 0 when things look ’normal’. The Anomaly Advisor leverages this information to enable seamless space or room level anomaly detection out of the box with minimal configuration.</p>CNCF Live: Machine Learning Anomaly Detectionhttps://www.netdata.cloud/blog/cncf-live-power-up-your-machine-learning-automated-anomaly-detection/Wed, 27 Apr 2022 00:00:00 +0000https://www.netdata.cloud/blog/cncf-live-power-up-your-machine-learning-automated-anomaly-detection/<h2 id="join-us-live-to-talk-ml">Join Us Live to Talk ML</h2> <p>Join ML Lead Andrew Maguire and Product Manager Shyam Sreevalsan on the 23rd of June at 5pm UTC for the Netdata Machine Learning Meetup, which will be livestreamed on Youtube. In this session, we will demo and preview new and future features, along with a Q&A, and discuss the following topics:</p> <ul> <li>The role of Machine Learning in DevOps Infrastructure Monitoring & Troubleshooting</li> <li>The Netdata way of approaching Machine Learning</li> <li>Challenges of building Macvhine learning solutions that are useful and user friendly.</li> </ul> Feel free to ask questions or share ideas on <a href="https://discord.gg/ZyeDHQTdaW">our Community Discord</a>, or <a href="https://(https://www.meetup.com/netdata-infrastructure-monitoring-meetup-group/events/286243158/">RSVP to the event</a>. We look forward to seeing you then! <h2 id="cncf-live-power-up-your-machine-learning---automated-anomaly-detection">CNCF Live: Power up your machine learning - Automated anomaly detection</h2> <p>Our Analytics & ML lead Andrew Maguire recently had a chance to share our new <a href="https://community.netdata.cloud/t/anomaly-advisor-beta-launch/2717">Anomaly Advisor</a> feature with the wider CNCF community. In his demonstration he did some light chaos engineering (using <a href="https://www.gremlin.com/">Gremlin</a> and <a href="https://wiki.ubuntu.com/Kernel/Reference/stress-ng">stress-ng</a>) to generate some real anomalies on his infrastructure and watch how it all played out in the Anomaly Advisor in Netdata Cloud.</p>Netdata vs Chronosphere | Monitoring Tools Comparisonhttps://www.netdata.cloud/comparisons/chronosphere/Mon, 01 Jan 0001 00:00:00 +0000https://www.netdata.cloud/comparisons/chronosphere/Netdata provides edge-native observability with per-second granularity, automatic ML anomaly detection, and transparent pricing—eliminating PromQL learning curves and SaaS-only limitations that challenge Chronosphere users.