Parallel and Iterative Processing of Machine Learning Recommendations with Spark - Free Code Fridays

Video created by gdemarest on Jun 22, 2016

    Recommendation systems help narrow your choices to those that best meet your particular needs. They are among the most popular applications of big data processing. In this Free Code Friday session, you’ll learn how to build a recommendation model from movie ratings using an iterative algorithm and parallel processing with Apache Spark MLlib.

    carol mcdonald, Solution Architect and HBase/Hadoop Instructor at MapR, covers:

    • A key difference between Apache Spark and MapReduce, which makes Spark much faster for iterative algorithms,
    • Loading and exploring the sample data set with Spark,
    • Using Spark MLlib’s Alternating Least Squares algorithm to make movie recommendations, and
    • Testing the results of the recommendations.

     

    More Resources:

    Ebook: Getting Started with Apache Spark: From Inception to Production
    Slides to the session
    Blog post on the topic