diff options
Diffstat (limited to 'docs/guides/monitor/visualize-monitor-anomalies.md')
| -rw-r--r-- | docs/guides/monitor/visualize-monitor-anomalies.md | 18 |
1 files changed, 8 insertions, 10 deletions
diff --git a/docs/guides/monitor/visualize-monitor-anomalies.md b/docs/guides/monitor/visualize-monitor-anomalies.md index 681ba8390..9eace5232 100644 --- a/docs/guides/monitor/visualize-monitor-anomalies.md +++ b/docs/guides/monitor/visualize-monitor-anomalies.md @@ -1,4 +1,4 @@ -<!-- +--- title: "Monitor and visualize anomalies with Netdata (part 2)" description: "Using unsupervised anomaly detection and machine learning, get notified " image: /img/seo/guides/monitor/visualize-monitor-anomalies.png @@ -6,13 +6,11 @@ author: "Joel Hans" author_title: "Editorial Director, Technical & Educational Resources" author_img: "/img/authors/joel-hans.jpg" custom_edit_url: https://github.com/netdata/netdata/edit/master/docs/guides/monitor/visualize-monitor-anomalies.md ---> - -# Monitor and visualize anomalies with Netdata (part 2) +--- Welcome to part 2 of our series of guides on using _unsupervised anomaly detection_ to detect issues with your systems, containers, and applications using the open-source Netdata Agent. For an introduction to detecting anomalies and -monitoring associated metrics, see [part 1](/docs/guides/monitor/anomaly-detection.md), which covers prerequisites and +monitoring associated metrics, see [part 1](/docs/guides/monitor/anomaly-detection-python.md), which covers prerequisites and configuration basics. With anomaly detection in the Netdata Agent set up, you will now want to visualize and monitor which charts have @@ -50,8 +48,8 @@ analysis (RCA). The anomalies collector creates two "classes" of alarms for each chart captured by the `charts_regex` setting. All these alarms are preconfigured based on your [configuration in -`anomalies.conf`](/docs/guides/monitor/anomaly-detection.md#configure-the-anomalies-collector). With the `charts_regex` -and `charts_to_exclude` settings from [part 1](/docs/guides/monitor/anomaly-detection.md) of this guide series, the +`anomalies.conf`](/docs/guides/monitor/anomaly-detection-python.md#configure-the-anomalies-collector). With the `charts_regex` +and `charts_to_exclude` settings from [part 1](/docs/guides/monitor/anomaly-detection-python.md) of this guide series, the Netdata Agent creates 32 alarms driven by unsupervised anomaly detection. The first class triggers warning alarms when the average anomaly probability for a given chart has stayed above 50% for @@ -81,7 +79,7 @@ alarms for any dimension on the `anomalies_local.probability` and `anomalies_loc In either [Netdata Cloud](https://app.netdata.cloud) or the local Agent dashboard at `http://NODE:19999`, click on the **Anomalies** [section](/web/gui/README.md#sections) to see the pair of anomaly detection charts, which are preconfigured to visualize per-second anomaly metrics based on your [configuration in -`anomalies.conf`](/docs/guides/monitor/anomaly-detection.md#configure-the-anomalies-collector). +`anomalies.conf`](/docs/guides/monitor/anomaly-detection-python.md#configure-the-anomalies-collector). These charts have the contexts `anomalies.probability` and `anomalies.anomaly`. Together, these charts create meaningful visualizations for immediately recognizing not only that something is going wrong on your node, but @@ -90,7 +88,7 @@ give context as to where to look next. The `anomalies_local.probability` chart shows the probability that the latest observed data is anomalous, based on the trained model. The `anomalies_local.anomaly` chart visualizes 0→1 predictions based on whether the latest observed data is anomalous based on the trained model. Both charts share the same dimensions, which you configured via -`charts_regex` and `charts_to_exclude` in [part 1](/docs/guides/monitor/anomaly-detection.md). +`charts_regex` and `charts_to_exclude` in [part 1](/docs/guides/monitor/anomaly-detection-python.md). In other words, the `probability` chart shows the amplitude of the anomaly, whereas the `anomaly` chart provides quick yes/no context. @@ -126,7 +124,7 @@ the dashboard into only the charts relevant to what you're seeing from the anoma ## What's next? -Between this guide and [part 1](/docs/guides/monitor/anomaly-detection.md), which covered setup and configuration, you +Between this guide and [part 1](/docs/guides/monitor/anomaly-detection-python.md), which covered setup and configuration, you now have a fundamental understanding of how unsupervised anomaly detection in Netdata works, from root cause to alarms to preconfigured or custom dashboards. |