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Anomaly Detection


Automate Anomaly Detection for Time Series Data

This article was originally published in The New Stack and is reposted here with permission. Hundreds of billions of sensors produce vast amounts of time series data every day. The sheer volume of data that companies collect makes it challenging to analyze and glean insights. Machine learning drastically accelerates time series data analysis so that companies can understand and act on their time series data to drive significant innovation and improvements.


Anomaly Detection and AIOps - Your On-Call Assistant for Intelligent Alerting and Root Cause Analysis

In this blog, we examine how anomaly detection helps by setting up healthy alerts and providing efficient root cause analysis. Anomaly detection, part of AIOps, guides your attention to the places and times where remarkable things occurred. It reduces information overload, thereby speeding up RCA investigation.


Expedite infrastructure investigations with Kubernetes Anomalies

Modern Kubernetes environments are becoming increasingly complex. In 2021, Datadog analyzed real-world usage data from more than 1.5 billion containers and found that the average number of pods per organization had doubled over the course of two years. Organizations running containers also tend to deploy more monitors than companies that don’t leverage containers, pointing to the increased need for monitoring in these environments.


Common Anomaly Detection Challenges & How To Solve Them

Anomaly detection can be defined by data points or events that deviate away from its normal behavior. If you think of this in the context of time-series continuous datasets, the normal or expected value is going to be the baseline, and the limits around it represent the tolerance associated with the variance. If a new value deviates above or below these limits, then that data point can be considered anomalous.


Time Series Forecasting Use Cases and Anomaly Detection

Wouldn’t it be great to peek into the future and find answers to the problems that you’re facing today? This may sound like science fiction, but many companies currently possess this capability, and they are creating strategies around it to strengthen their monitoring and analytical capabilities. One way is time series forecasting, a statistical method. You can take advantage of the insights of time series forecasting by using techniques like anomaly detection to gain.


Anomaly rate in every chart

A month ago, we introduced unsupervised ML & Anomaly Detection in Netdata, the Anomaly Advisor. 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.


Test Driving Machine Learning (ML) Anomaly Advisor

Netdata’s new Anomaly Advisor feature lets you quickly identify potentially anomalous metrics during a particular timeline of interest. This results in considerably speeding up your troubleshooting workflow and saving valuable time when faced with an outage or issue you are trying to root cause.

Anomaly Detection Models in Moogsoft | Moogsoft Product Videos & How-Tos

Moogsoft has several different anomaly detectors, and auto-select the optimum one for given metrics. This video explains each model, as well as how to override the model selected by default. Don't forget to subscribe for content on DevOps, Observability, AIOps and more!

Netdata Machine Learning Meetup

This video livestream meetup by Netdata takes a deep dive into the fundamentals of Machine Learning in DevOps Infrastructure Monitoring. It also covers the Netdata way of approaching Machine Learning. The Anomaly Advisor major update to Netdata is introduced as a valuable troubleshooting tool for any DevOps or Site Reliability Engineer looking for anomalies in their infrastructure. The hosts share real-world infrastructure monitoring & troubleshooting examples, as well as early feedback from the community on the Anomaly Advisor.