Parallel and Iterative Processing for Machine Learning Recommendations with Spark

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, HBase Hadoop Instructor at MapR, will cover:

  • 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