The document discusses applying machine learning models to real-time data streams. It covers building analytic models from historical data using tools like R, TensorFlow and Hadoop. It then discusses applying these pre-built models to real-time streaming data using frameworks like Apache Spark, TIBCO StreamBase and H2O to power applications like predictive maintenance and manufacturing analytics. The key takeaway is that machine learning on historical data finds insights, while stream processing applies these models in real-time to drive closed-loop analytics and enable real-time action.