Training and Prediction in Spark Streaming Machine Learning Model

Question

I am having a hard time understanding how we can both update the machine learning model and use it to make predictions in one spark streaming job.

This code is from Spark StreamingLinearRegressionExample class

val trainingData = ssc.textFileStream(args(0)).map(LabeledPoint.parse).cache()
val testData = ssc.textFileStream(args(1)).map(LabeledPoint.parse)

val numFeatures = 3
val model = new StreamingLinearRegressionWithSGD()
  .setInitialWeights(Vectors.zeros(numFeatures))

model.trainOn(trainingData)
model.predictOnValues(testData.map(lp => (lp.label, lp.features))).print()

The variable model is being updated in one stream and used to predict in another stream. I do not understand how the changes were done to the model in the trainOn method are reflected in predictOnValues.