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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):

Manpreet Singh Ghotra

Rajdeep Dua

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Table of Contents (13) Chapters

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

Performance improvements in Spark ML over Spark MLlib

Spark 2.0 uses Tungsten Engine, which is built using ideas of modern compilers and MPP databases. It emits optimized bytecode at runtime, which collapses the query into a single function. Hence, there is no need for virtual function calls. It also uses CPU registers to store intermediate data. This technique has been called whole stage code generation.

Reference : https://databricks.com/blog/2016/05/11/apache-spark-2-0-technical-preview-easier-faster-and-smarter.htmlSource: https://databricks.com/blog/2016/05/11/apache-spark-2-0-technical-preview-easier-faster-and-smarter.html

The upcoming table and graph show single function improvements between Spark 1.6 and Spark 2.0:

Chart comparing Performance improvements in Single line functions between Spark 1.6 and Spark 2.0

Table comparing Performance improvements in Single line functions between Spark 1.6 and Spark...

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Authors (2)

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

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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