Book Image

Machine Learning with Spark - Second Edition

By : Rajdeep Dua, Manpreet Singh Ghotra
Book Image

Machine Learning with Spark - Second Edition

By: Rajdeep Dua, Manpreet Singh Ghotra

Overview of this book

This book will teach you about popular machine learning algorithms and their implementation. You will learn how various machine learning concepts are implemented in the context of Spark ML. You will start by installing Spark in a single and multinode cluster. Next you'll see how to execute Scala and Python based programs for Spark ML. Then we will take a few datasets and go deeper into clustering, classification, and regression. Toward the end, we will also cover text processing using Spark ML. Once you have learned the concepts, they can be applied to implement algorithms in either green-field implementations or to migrate existing systems to this new platform. You can migrate from Mahout or Scikit to use Spark ML. By the end of this book, you will acquire the skills to leverage Spark's features to create your own scalable machine learning applications and power a modern data-driven business.
Table of Contents (13 chapters)

Using the recommendation model

Now that we have our trained model, we're ready to use it to make predictions.

ALS Model recommendations

Starting Spark v2.0, org.apache.spark.ml.recommendation.ALS modeling is a blocked implementation of the factorization algorithm that groups "users" and "products" factors into blocks and decreases communication by sending only one copy of each user vector to each product block at each iteration, and only for the product blocks that need that user's feature vector.

Here, we will load the rating data from the movies dataset where each row consists of a user, movie, rating, and a timestamp. We will then train an ALS model by default works on explicit preferences (implicitPrefs is false). We will evaluate...