New model export and PMML markup in Spark 2.0

In this recipe, we explore the model export facility available in Spark 2.0 to use Predictive Model Markup Language (PMML). This standard XML-based language allows you to export and run your models on other systems (some limitations apply). You can explore the There's more... section for more information.

How to do it...

1. Start a new project in IntelliJ or in an IDE of your choice. Make sure that the necessary JAR files are included.
2. Set up the package location where the program will reside:

package spark.ml.cookbook.chapter4

3. Import the necessary packages for SparkContext to get access to the cluster:

import org.apache.spark.mllib.linalg.Vectors
import org.apache.spark.sql.SparkSession
import org.apache.spark.mllib.clustering.KMeans

4. Create Spark's configuration and SparkContext:

val spark = SparkSession
.builder
.master("local[*]")   // if use cluster master("spark://master:7077")
.appName("myPMMLExport")
.config("spark.sql.warehouse.dir", ".")
.getOrCreate...