Book Image

Apache Spark 2.x Machine Learning Cookbook

By : Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
Book Image

Apache Spark 2.x Machine Learning Cookbook

By: Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

Overview of this book

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction


In every line of business ranging from running a small business to creating and managing a mission critical application, there are a number of tasks that are common and need to be included as a part of almost every workflow that is required during the course of executing the functions. This is true even for building robust machine learning systems. In Spark machine learning, some of these tasks range from splitting the data for model development (train, test, validate) to normalizing input feature vector data to creating ML pipelines via the Spark API. We provide a set of recipes in this chapter to enable the reader to think about what is actually required to implement an end-to-end machine learning system.

This chapter attempts to demonstrate a number of common tasks which are present in any robust Spark machine learning system implementation. To avoid redundant references these common tasks in every recipe covered in this book, we have factored out such common tasks as short...