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Machine Learning with Spark

You're reading from Machine Learning with Spark Develop intelligent, distributed machine learning systems

Product type Paperback

Published in Apr 2017

Publisher Packt

ISBN-13 9781785889936

Length 532 pages

Edition 2nd Edition

Languages

Scala

Tools

Apache Spark

Concepts

Machine Learning

Authors (2):

Dua

Ghotra

View More author details

Table of Contents (19) Chapters

Title Page

Credits

About the Authors

About the Reviewer

www.PacktPub.com

Customer Feedback

Preface

1. Getting Up and Running with Spark FREE CHAPTER

2. Math for Machine Learning

3. Designing a Machine Learning System

4. Obtaining, Processing, and Preparing Data with Spark

5. Building a Recommendation Engine with Spark

6. Building a Classification Model with Spark

7. Building a Regression Model with Spark

8. Building a Clustering Model with Spark

9. Dimensionality Reduction with Spark

10. Advanced Text Processing with Spark

11. Real-Time Machine Learning with Spark Streaming

12. Pipeline APIs for Spark ML

UI in Spark

Spark provides a web interface which can be used to monitor jobs, see the environment, and run SQL commands.

SparkContext launches a web UI on port 4040 that displays useful information about the application. This includes the following:

A list of scheduler stages and tasks
A summary of RDD sizes and memory usage
Environmental information
Information about the running executors

This interface can be accessed by going to http://<driver-node>:4040 in a web browser. If multiple SparkContexts are running on the same host, they will bind to ports beginning with port 4040 (4041, 4042, and so on).

The following screenshots display some of the information provided by the Web UI:

UI showing the Environment of the Spark Content

UI table showing Executors available

You have been reading a chapter from

Machine Learning with Spark - Second Edition

Published in: Apr 2017

Publisher: Packt

ISBN-13: 9781785889936

© 2017 Packt Publishing Limited All Rights Reserved

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Authors (2)

Dua

Dua

Rajdeep Dua has over 18 years experience in the cloud and big data space. He has taught Spark and big data at some of the most prestigious tech schools in India: IIIT Hyderabad, ISB, IIIT Delhi, and Pune College of Engineering. He currently leads the developer relations team at Salesforce India. He has also presented BigQuery and Google App Engine at the W3C conference in Hyderabad. He led the developer relations teams at Google, VMware, and Microsoft, and has spoken at hundreds of other conferences on the cloud. Some of the other references to his work can be seen at Your Story and on ACM digital library. His contributions to the open source community relate to Docker, Kubernetes, Android, OpenStack, and Cloud Foundry.

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Ghotra

Ghotra

Manpreet Singh Ghotra has more than 15 years experience in software development for both enterprise and big data software. He is currently working at Salesforce on developing a machine learning platform/APIs using open source libraries and frameworks such as Keras, Apache Spark, and TensorFlow. He has worked on various machine learning systems, including sentiment analysis, spam detection, and anomaly detection. He was part of the machine learning group at one of the largest online retailers in the world, working on transit time calculations using Apache Mahout, and the R recommendation system, again using Apache Mahout. With a master's and postgraduate degree in machine learning, he has contributed to, and worked for, the machine learning community.

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