IGNITE: Practical AI with Machine Learning for Observability
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.
Speaker
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Costa TsaousisNetdataCosta Tsaousis, is the Founder and CEO of Netdata. Since 1995, Costa has been actively working on internet related startups. He has been a co-founder and C-level executive of many successful projects, including Internet Service Providers, Cloud Hosting Providers and Fintech startups. With a passion for innovation and open source, he now leads Netdata, a monitoring solution aiming to simplify and modernize infrastructure observability for all of us.