Book Image

Apache Spark Machine Learning Blueprints

By : Alex Liu
Book Image

Apache Spark Machine Learning Blueprints

By: Alex Liu

Overview of this book

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.
Table of Contents (18 chapters)
Apache Spark Machine Learning Blueprints
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Index

Deployment


Some users may have some deployment systems in place already for which exporting the developed models to users' desired forms could be good enough.

For linear regression and logistic regression, MLlib supports model exporting to Predictive Model Markup Language (PMML).

For more information about exporting to PMML from MLlib, visit https://spark.apache.org/docs/latest/mllib-pmml-model-export.html.

For the R notebook, it can be run on another environment directly. Also, with the R package PMML, R models can be exported.

For more information on the R package PMML, go to http://journal.r-project.org/archive/2009-1/RJournal_2009-1_Guazzelli+et+al.pdf.

It is also possible to deploy the models for decision making directly on Apache Spark and make the results easily available to users.

Two commonly used methods of deploying results are (1) dashboard and (2) rule-based decision making. Which one to select depends on who we will supply our result to.

Here, we will discuss them only briefly as...