  In this paper, we present iPrescribe, a scalable low-latency architecture for
recommending 'next-best-offers' in an online setting. The paper presents the
design of iPrescribe and compares its performance for implementations using
different real-time streaming technology stacks. iPrescribe uses an ensemble of
deep learning and machine learning algorithms for prediction. We describe the
scalable real-time streaming technology stack and optimized machine-learning
implementations to achieve a 90th percentile recommendation latency of 38
milliseconds. Optimizations include a novel mechanism to deploy recurrent Long
Short Term Memory (LSTM) deep learning networks efficiently.
