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Essential PySpark for Scalable Data Analytics

You're reading from   Essential PySpark for Scalable Data Analytics A beginner's guide to harnessing the power and ease of PySpark 3

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Product type Paperback
Published in Oct 2021
Publisher Packt
ISBN-13 9781800568877
Length 322 pages
Edition 1st Edition
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Author (1):
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Sreeram Nudurupati Sreeram Nudurupati
Author Profile Icon Sreeram Nudurupati
Sreeram Nudurupati
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Data Engineering
2. Chapter 1: Distributed Computing Primer FREE CHAPTER 3. Chapter 2: Data Ingestion 4. Chapter 3: Data Cleansing and Integration 5. Chapter 4: Real-Time Data Analytics 6. Section 2: Data Science
7. Chapter 5: Scalable Machine Learning with PySpark 8. Chapter 6: Feature Engineering – Extraction, Transformation, and Selection 9. Chapter 7: Supervised Machine Learning 10. Chapter 8: Unsupervised Machine Learning 11. Chapter 9: Machine Learning Life Cycle Management 12. Chapter 10: Scaling Out Single-Node Machine Learning Using PySpark 13. Section 3: Data Analysis
14. Chapter 11: Data Visualization with PySpark 15. Chapter 12: Spark SQL Primer 16. Chapter 13: Integrating External Tools with Spark SQL 17. Chapter 14: The Data Lakehouse 18. Other Books You May Enjoy

Chapter 10: Scaling Out Single-Node Machine Learning Using PySpark

In Chapter 5, Scalable Machine Learning with PySpark, you learned how you could use the power of Apache Spark's distributed computing framework to train and score machine learning (ML) models at scale. Spark's native ML library provides good coverage of standard tasks that data scientists typically perform; however, there is a wide variety of functionality provided by standard single-node Python libraries that were not designed to work in a distributed manner. This chapter deals with techniques for horizontally scaling out standard Python data processing and ML libraries such as pandas, scikit-learn, XGBoost, and more. It also covers scaling out of typical data science tasks such as exploratory data analysis (EDA), model training, model inferencing, and, finally, also covers a scalable Python library named Koalas that lets you effortlessly write PySpark code using the very familiar and easy-to-use pandas-like...

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