Network-based threats to personal information, company secrets and critical infrastructure are increasing in frequency and sophistication. A rapid response is critical for stopping these threats, but, with network speeds reaching 100Gb/s and beyond, network defenders are swamped by overwhelming amounts of traffic, hindering both detection and response. Data scientists maintain that machine learning has the potential to help fight these threats, but the current dominant “Big Data” strategy of backhauling data to a central repository, using tools such as Hadoop and Spark, adds significant latency while incurring additional costs for bandwidth, storage, and computational resources. Furthermore, “Big Data” platforms such as Spark are optimized primarily for data management and model training, not serving trained models in deployment. We will walk through the process of translating a multi-dimensional anomaly detection algorithm that operates in batch into a streaming algorithm suitable for deployment in a network sensor. Then we demonstrate how deploying these models with Redis and the Redis ML module leads to dramatic reduction in processing time, leading to the faster threat detection and response.