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Mastering Machine Learning with Spark 2.x

You're reading from   Mastering Machine Learning with Spark 2.x Harness the potential of machine learning, through spark

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Product type Paperback
Published in Aug 2017
Publisher Packt
ISBN-13 9781785283451
Length 340 pages
Edition 1st Edition
Languages
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Authors (3):
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Alex Tellez Alex Tellez
Author Profile Icon Alex Tellez
Alex Tellez
Michal Malohlava Michal Malohlava
Author Profile Icon Michal Malohlava
Michal Malohlava
Max Pumperla Max Pumperla
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Max Pumperla
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Toc

Preparation of the environment

In this Chapter, instead of using Spark shell, we will build two standalone Spark applications using Scala API: one for model preparation and the second for model deployment. In the case of Spark, the Spark application is a normal Scala application with a main method that serves as an entry point for execution. For example, here is a skeleton of application for model training:

object Chapter8 extends App {

val spark = SparkSession.builder()
.master("local[*]")
.appName("Chapter8")
.getOrCreate()

val sc = spark.sparkContext
sc.setLogLevel("WARN")
script(spark, sc, spark.sqlContext)


def script(spark: SparkSession, sc: SparkContext, sqlContext: SQLContext): Unit = {
// ...code of application } }

Moreover, we will try to extract parts, which can be shared between both applications, into a library. This will...

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