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

Repeatability and automation


In this section, we will discuss some methods of organizing datasets, preprocessing into workflows, and then use the Apache Spark pipeline to represent as well as implement these workflows. Then, we will review data preprocessing automation solutions.

After this section, we will be able to use Spark pipelines to represent and implement datasets preprocessing workflows and understand some automation solutions made available by Apache Spark.

Dataset preprocessing workflows

Our data preparation work from Data cleaning to Identity matching to Data re-organization to Feature extraction were organized in a way to reflect our step-by-step orderly process of preparing datasets for machine learning. In other words, all the data preparation work can be organized into a workflow.

Organizing data cleaning into workflows can help achieve repeatability and also possible automation, which is often the most valuable for machine learning professionals as ML professionals and data...