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Machine Learning with Apache Spark Quick Start Guide
Machine Learning with Apache Spark Quick Start Guide

Machine Learning with Apache Spark Quick Start Guide: Uncover patterns, derive actionable insights, and learn from big data using MLlib

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Paperback Dec 2018 240 pages 1st Edition
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Paperback Dec 2018 240 pages 1st Edition
eBook
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Machine Learning with Apache Spark Quick Start Guide

Setting Up a Local Development Environment

In this chapter, we will install, configure, and deploy a local analytical development environment by provisioning a self-contained single-node cluster that will allow us to do the following:

  • Prototype and develop machine learning models and pipelines in Python
  • Demonstrate the functionality and usage of Apache Spark's machine learning library, MLlib, via the Spark Python API (PySpark)
  • Develop and test machine learning models on a single-node cluster using small sample datasets, and thereafter scale up to multi-node clusters processing much larger datasets with little or no code changes required

Our single-node cluster will host the following technologies:

CentOS Linux 7 virtual machine

First of all, we will assume that you have access to a physical or virtual machine provisioned with the CentOS 7 operating system. CentOS 7 is a free Linux distribution derived from Red Hat Enterprise Linux (RHEL). It is commonly used, along with its licensed upstream parent, RHEL, as the operating system of choice for Linux-based servers, since it is stable and backed by a large active community with detailed documentation. All the commands that we will use to install the various technologies listed previously will be Linux shell commands to be executed on a single CentOS 7 (or RHEL) machine, whether physical or virtual. If you do not have access to a CentOS 7 machine, then there are quite a few options available to provision a CentOS 7 virtual machine:

  • Cloud computing platforms such as Amazon Web Services (AWS), Microsoft Azure, and the Google...

Summary

In this chapter, we have installed, configured, and deployed a local analytical development environment consisting of a single-node Apache Spark 2.3.2 and Apache Kafka 2.0.0 cluster that will also allow us to interactively develop Spark applications using Python 3.6 via Jupyter Notebook.

In the next chapter, we will discuss some of the high-level concepts behind common artificial intelligence and machine learning algorithms, as well as introducing Apache Spark's machine learning library, MLlib!

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Key benefits

  • Make a hands-on start in the fields of Big Data, Distributed Technologies and Machine Learning
  • Learn how to design, develop and interpret the results of common Machine Learning algorithms
  • Uncover hidden patterns in your data in order to derive real actionable insights and business value

Description

Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data.

Who is this book for?

This book is aimed at Business Analysts, Data Analysts and Data Scientists who wish to make a hands-on start in order to take advantage of modern Big Data technologies combined with Advanced Analytics.

What you will learn

  • Understand how Spark fits in the context of the big data ecosystem
  • Understand how to deploy and configure a local development environment using Apache Spark
  • Understand how to design supervised and unsupervised learning models
  • Build models to perform NLP, deep learning, and cognitive services using Spark ML libraries
  • Design real-time machine learning pipelines in Apache Spark
  • Become familiar with advanced techniques for processing a large volume of data by applying machine learning algorithms

Product Details

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Dec 26, 2018
Length: 240 pages
Edition : 1st
Language : English
ISBN-13 : 9781789346565
Vendor :
Apache
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Languages :
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Product Details

Publication date : Dec 26, 2018
Length: 240 pages
Edition : 1st
Language : English
ISBN-13 : 9781789346565
Vendor :
Apache
Category :
Languages :
Concepts :

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Table of Contents

9 Chapters
The Big Data Ecosystem Chevron down icon Chevron up icon
Setting Up a Local Development Environment Chevron down icon Chevron up icon
Artificial Intelligence and Machine Learning Chevron down icon Chevron up icon
Supervised Learning Using Apache Spark Chevron down icon Chevron up icon
Unsupervised Learning Using Apache Spark Chevron down icon Chevron up icon
Natural Language Processing Using Apache Spark Chevron down icon Chevron up icon
Deep Learning Using Apache Spark Chevron down icon Chevron up icon
Real-Time Machine Learning Using Apache Spark Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon
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