stackconf 2024 | IGNITE: Practical AI with Machine Learning for Observability by Costa Tsaousis
Get 25% off your ticket with the code YOUTUBE25 – experience the talks live at stackconf.eu!
Machine Learning for observability can be challenging, given the uniqueness of each workload. However, we can leverage ML to detect individual component anomalies, even if they are sometimes noisy/imprecise. At Netdata, we use ML models to analyze the behaviour of individual metrics. These models adapt to the specific characteristics of each metric, ensuring anomalies can be detected accurately, even in unique workloads. The power of ML becomes evident when these seemingly noisy anomalies converge across various services, serving as indicators of something exceedingly unusual. ML is an advisor, training numerous independent models for each individually collected metric to achieve anomaly detection based on recent behaviour. When multiple independent metrics exhibit anomalies simultaneously, it is usually a signal that something unusual is occurring. This approach to ML can be instrumental in uncovering malicious attacks and, in many cases, predicting combined failures across seemingly unrelated components.
NETWAYS
Konferenzen: https://www.netways.de/events
Schulungen: https://www.netways.de/schulungen
Shop: https://shop.netways.de/
Blog: http://blog.netways.de/
NWS: https://nws.netways.de
Webinare
Archiv Link: https://www.netways.de/webinare/archi...
Aktuell: https://www.netways.de/wb
Social Media
SlideShare: http://de.slideshare.net/netways
YouTube: https://www.netways.de/youtube
Facebook: / netways
X: / netwaysevents
Instagram: / netwaysgmbh
Gemafreie Musik von www.frametraxx.de