We'll describe how deep learning can be applied to detect anomalies, such as network intrusions, in a production environment. In part one of the talk, we'll build an end-to-end data pipeline using Hadoop for storage, Streamsets for data flow, Spark for distributed GPUs, and Deeplearning4j for anomaly detection. In part two, we'll showcase a demo environment that demonstrates how a deep net uncovers anomalies. This visualization will illustrate how system administrators can view malicious behavior and prioritize efforts to stop attacks. It's assumed that registrants are familiar with popular big data frameworks on the JVM.