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

Chapter 3. A Holistic View on Spark

After setting the Apache Spark system up as per Chapter 1, Spark for Machine Learning, and completing our data preparation work according to what we discussed in Chapter 2, Data Preparation for Spark ML, we will now move to a new stage of utilizing Apache Spark-based systems to turn data into insight.

According to the research done by Gartner and others, many organizations lost a large amount of value simply due to the lack of a holistic view of their business. In this chapter, we will review machine learning methods and processes of obtaining a holistic view of business. Then, we will discuss how Apache Spark fits in to making the related computing easy and fast and, at the same time, illustrate this process of developing holistic views from data using Apache Spark computing with one real-life example step by step.

  • Spark for a holistic view

  • Methods for a holistic view

  • Feature preparation

  • Model estimation

  • Model evaluation

  • Results explanation

  • Deployment