Spark is recognized as a great analytics engine. One of its benefits is its inclusion of machine learning algorithms that can group, predict, classify and even recommend based on the analysis of a corpus of data. Once the model is created, it can be applied to new data. In some situations, there is a need to score the model while the data is streaming to decide what to do with the new data.
In this latest Data Science Central Webinar event, you will learn how to process the data to arrive at a model and how the resulting model can be used to score streaming data using IBM Streams.
Speaker: Jacques Roy, Worldwide Product and Strategy — IBM
Hosted by: Bill Vorhies, Editorial Director — Data Science Central
