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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):

Manpreet Singh Ghotra

Rajdeep Dua

View More author details

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

SchemaRDD

SchemaRDD is a combination of RDD and schema information. It also offers many rich and easy-to-use APIs (that is, the DataSet API). SchemaRDD is not used with 2.0 and is internally used by DataFrame and Dataset APIs.

A schema is used to describe how structured data is logically organized. After obtaining the schema information, the SQL engine is able to provide the structured query capability for the corresponding data. The DataSet API is a replacement for Spark SQL parser's functions. It is an API to achieve the original program logic tree. Subsequent processing steps reuse Spark SQL's core logic. We can safely consider DataSet API's processing functions as completely equivalent to that of SQL queries.

SchemaRDD is an RDD subclass. When a program calls the DataSet API, a new SchemaRDD object is created, and a logic plan attribute of the new object is created by adding a new logic operation node on the original logic plan tree. Operations of the DataSet API (like RDD) are of two types--Transformation and Action.

APIs related to the relational operations are attributed to the Transformation type.

Operations associated with data output sources are of Action type. Like RDD, a Spark job is triggered and delivered for cluster execution, only when an Action type operation is called.

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Machine Learning with Spark - Second Edition

Published in: Apr 2017

Publisher: Packt

ISBN-13: 9781785889936

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Authors (2)

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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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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