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Apache Mahout Essentials

You're reading from Apache Mahout Essentials Implement top-notch machine learning algorithms for classification, clustering, and recommendations with Apache Mahout

Product type Paperback

Published in Jun 2015

Publisher

ISBN-13 9781783554997

Length 164 pages

Edition 1st Edition

Languages

Java

Tools

Mahout

Concepts

Machine Learning

Author (1):

Jayani Withanawasam

View More author details

Table of Contents (8) Chapters

Preface

1. Introducing Apache Mahout FREE CHAPTER

2. Clustering

3. Regression and Classification

4. Recommendations

5. Apache Mahout in Production

6. Visualization

Index

Chapter 5. Apache Mahout in Production

This chapter talks about achieving scalability in Apache Mahout with an Apache Hadoop ecosystem.

In this chapter, we will cover the following topics:

Key components of Apache Hadoop
The life cycle of a Hadoop application
Setting up Hadoop
- Local mode
- The pseudo-distributed mode
- The fully-distributed mode
Setting up Apache Mahout with Hadoop
Monitoring Hadoop
Troubleshooting Hadoop
Optimization tips

The rest of the chapter is locked

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Authors (1)

Jayani Withanawasam

Jayani Withanawasam

Jayani Withanawasam is R&D engineer and a senior software engineer at Zaizi Asia, where she focuses on applying machine learning techniques to provide smart content management solutions. She is currently pursuing an MSc degree in artificial intelligence at the University of Moratuwa, Sri Lanka, and has completed her BE in software engineering (with first class honors) from the University of Westminster, UK. She has more than 6 years of industry experience, and she has worked in areas such as machine learning, natural language processing, and semantic web technologies during her tenure. She is passionate about working with semantic technologies and big data.

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