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Machine Learning with LightGBM and Python

You're reading from   Machine Learning with LightGBM and Python A practitioner's guide to developing production-ready machine learning systems

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Product type Paperback
Published in Sep 2023
Publisher Packt
ISBN-13 9781800564749
Length 252 pages
Edition 1st Edition
Languages
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Author (1):
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Andrich van Wyk Andrich van Wyk
Author Profile Icon Andrich van Wyk
Andrich van Wyk
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Toc

Table of Contents (17) Chapters Close

Preface 1. Part 1: Gradient Boosting and LightGBM Fundamentals
2. Chapter 1: Introducing Machine Learning FREE CHAPTER 3. Chapter 2: Ensemble Learning – Bagging and Boosting 4. Chapter 3: An Overview of LightGBM in Python 5. Chapter 4: Comparing LightGBM, XGBoost, and Deep Learning 6. Part 2: Practical Machine Learning with LightGBM
7. Chapter 5: LightGBM Parameter Optimization with Optuna 8. Chapter 6: Solving Real-World Data Science Problems with LightGBM 9. Chapter 7: AutoML with LightGBM and FLAML 10. Part 3: Production-ready Machine Learning with LightGBM
11. Chapter 8: Machine Learning Pipelines and MLOps with LightGBM 12. Chapter 9: LightGBM MLOps with AWS SageMaker 13. Chapter 10: LightGBM Models with PostgresML 14. Chapter 11: Distributed and GPU-Based Learning with LightGBM 15. Index 16. Other Books You May Enjoy

LightGBM Models with PostgresML

In this chapter, we’ll look at a unique MLOps platform called PostgresML. PostgresML is a Postgres database extension that allows you to train and deploy ML models using SQL.

PostgresML and SQL are a significant departure from the scikit-learn style of programming we’ve used throughout this book. However, as we’ll see in this chapter, performing ML model development and deployment at the database level has significant advantages regarding data movement requirements and inferencing latency.

The main topics in this chapter are as follows:

  • An overview of PostgresML
  • Getting started with PostgresML
  • A customer churn case study with PostgresML and LightGBM
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