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Learn PySpark

Build Python-based Machine Learning and Deep Learning Models

  • Book
  • © 2019

Overview

Authors:
  1. Pramod Singh

    1. Bangalore, India

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  • Covers entire range of PySpark’s offerings from streaming to graph analytics
  • Build standardized work flows for pre-processing and builds machine learning and deep learning models on big data sets
  • Discusses how to schedule different Spark jobs using Airflow

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Table of contents (8 chapters)

  1. Front Matter

    Pages i-xviii
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  2. Introduction to Spark

    • Pramod Singh
    Pages 1-16

  3. Data Processing

    • Pramod Singh
    Pages 17-48

  4. Spark Structured Streaming

    • Pramod Singh
    Pages 49-65

  5. Airflow

    • Pramod Singh
    Pages 67-84

  6. MLlib: Machine Learning Library

    • Pramod Singh
    Pages 85-115

  7. Supervised Machine Learning

    • Pramod Singh
    Pages 117-159

  8. Unsupervised Machine Learning

    • Pramod Singh
    Pages 161-181

  9. Deep Learning Using PySpark

    • Pramod Singh
    Pages 183-203

  10. Back Matter

    Pages 205-210
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Keywords

About this book

Leverage machine and deep learning models to build applications on real-time data using PySpark. This book is perfect for those who want to learn to use this language to perform exploratory data analysis and solve an array of business challenges.
You'll start by reviewing PySpark fundamentals, such as Spark’s core architecture, and see how to use PySpark for big data processing like data ingestion, cleaning, and transformations techniques. This is followed by building workflows for analyzing streaming data using PySpark and a comparison of various streaming platforms. 


You'll then see how to schedule different spark jobs using Airflow with PySpark and book examine tuning machine and deep learning models for real-time predictions. This book concludes with a discussion on graph frames and performing network analysis using graph algorithms in PySpark. All the code presented in the book will be available in Python scripts on Github.


What You'll Learn
  • Develop pipelines for streaming data processing using PySpark 
  • Build Machine Learning & Deep Learning models using PySpark latest offerings
  • Use graph analytics using PySpark 
  • Create Sequence Embeddings from Text data 

Who This Book is For 



Data Scientists, machine learning and deep learning engineers who want to learn and use PySpark for real time analysis on streaming data.

Authors and Affiliations

  • Bangalore, India

    Pramod Singh

About the author

Pramod Singh is currently a Manager (Data Science) at Publicis Sapient and working as data science lead for a project with Mercedes Benz. He has spent the last nine years working on multiple Data projects at SapientRazorfish, Infosys & Tally and has used traditional to advanced machine learning and deep learning techniques in multiple projects using R, Python, Spark and Tensorflow. Pramod has also been a regular speaker at major conferences in India and abroad and is currently authoring a couple of books on Deep Learning and AI techniques. He regularly conducts Data Science meetups at SapientRazorfish and presents webinars on Machine Learning and Artificial Intelligence. He lives in Bangalore with his wife and 2-year-old son. In his spare time, he enjoys coding, reading and watching football.



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