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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

Summary

In this chapter, you were introduced to the end-to-end ML life cycle and the various steps involved in it. MLflow is a complete, end-to-end ML life cycle management tool. The MLflow Tracking component was presented, which is useful for streaming the ML experimentation process and helps you track all its attributes, including the data version, ML code, model parameters and metrics, and any other arbitrary artifacts. MLflow Model was introduced as a standards-based model format that provides model portability and reproducibility. MLflow Model Registry was also explored, which is a central model repository that supports the entire life cycle of a newly created model, from staging to production to archival. Model serving mechanisms, such as using batch and online processes, were also introduced. Finally, continuous delivery for ML was introduced. It is used to streamline the entire ML life cycle and automate the model life cycle using Model Registry features, such as the ability...

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