Running the Spark MLlib demo application
The Spark MLlib demo application demonstrates how to run machine-learning analytic jobs using Spark and Cassandra.
The Spark MLlib demo application demonstrates how to run machine-learning analytic jobs using Spark and Cassandra. The demo solves the classic iris flower classification problem, using the iris flower data set. The application will use the iris flower data set to build a Naive Bayes classifier that will recognize a flower based on four feature measurements.
Prerequisites
We strongly recommend that you install the BLAS library on your machines before running Spark MLlib jobs. For instructions on installing the BLAS library on your platform, see https://github.com/fommil/netlib-java/blob/master/README.md#machine-optimised-system-libraries.
The BLAS library is not distributed with DataStax Enterprise due to licensing restrictions, but improves MLlib performance significantly.
You must have the Gradle build tool installed to build the demo. See https://gradle.org/ for details on installing Gradle on your OS.