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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

Apache Mahout with Hadoop

Apache Mahout uses Apache Hadoop, which is a distributed computing framework, to achieve scalability. The following figure clearly shows the place where Apache Hadoop fits into Apache Mahout:

As shown in the previous figure, Yarn (Data processing) and HDFS (Data Storage) are key components in Apache Hadoop.

In this chapter, we will explain the important subcomponents of Yet Another Resource Negotiator (YARN) and HDFS and their behavior in detail before proceeding to the Hadoop installation steps.

YARN with MapReduce 2.0

First, let's understand YARN, which is a new addition to Apache Hadoop 2.0.

Earlier, Apache Hadoop operated with MapReduce 1.0. It had some drawbacks in cluster resource utilization due to the constraints incurred with the static allocation of map and reduce slots.

YARN, along with MapReduce 2.0, has overcome this drawback by inventing a novel, flexible resource allocation model that contains containers.

The YARN architecture consists of the following subcomponents...

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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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