Scaling AutoML-Driven Anomaly Detection With Luminaire

Description

Organizations rely heavily on time series metrics to measure and model key aspects of operational and business performance. The ability to reliably detect issues with these metrics is imperative to identifying early indicators of major problems before they become pervasive. This is a difficult machine learning and systems problem because temporal patterns are complex, ever changing, and often very noisy, traditionally requiring significant manual configuration and model maintenance. At Zillow, we have built an orchestration framework around Luminaire, our open-source python library for hands-off time-series Anomaly Detection. Luminaire provides a suite of models and built-in AutoML capabilities which we process with Spark for distributed training and scoring of thousands of metrics. In this talk, we will cover the architecture of this framework and performance of the Luminaire package across detection and prediction accuracy as well as runtime efficiency. Connect with us: Website: https://databricks.com Facebook: https://www.facebook.com/databricksinc Twitter: https://twitter.com/databricks LinkedIn: https://www.linkedin.com/company/databricks Instagram: https://www.instagram.com…

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