Twitter developed novel statistical techniques for automatically detecting anomalies in cloud infrastructure data. Specifically, the techniques employ statistical learning to detect anomalies in both application, and system metrics. • They employ time series decomposition to filter the trend and seasonal components of the time series. • They use robust statistical metrics – median and median absolute deviation (MAD) – to accurately detect anomalies, even in the presence of seasonal spikes. The techniques that Arun presents were evaluated with a wide variety of time series (system and application metrics obtained from production as well stock data) and have been deployed in production at Twitter. Arun demonstrates the efficacy of the proposed techniques using production data.