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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 9: Machine Learning Life Cycle Management

In the previous chapters, we explored the basics of scalable machine learning using Apache Spark. Algorithms dealing with supervised and unsupervised learning were introduced and their implementation details were presented using Apache Spark MLlib. In real-world scenarios, it is not sufficient to just train one model. Instead, multiple versions of the same model must be built using the same dataset by varying the model parameters to get the best possible model. Also, the same model might not be suitable for all applications, so multiple models are trained. Thus, it is necessary to track various experiments, their parameters, their metrics, and the version of the data they were trained on. Furthermore, models often drift, meaning that their prediction power decreases due to changes in the environment, so they need to be monitored and retrained when necessary.

This chapter will introduce the concepts of experiment tracking, model tuning...

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