Book Image

Machine Learning with LightGBM and Python

By : Andrich van Wyk
3 (1)
Book Image

Machine Learning with LightGBM and Python

3 (1)
By: Andrich van Wyk

Overview of this book

Machine Learning with LightGBM and Python is a comprehensive guide to learning the basics of machine learning and progressing to building scalable machine learning systems that are ready for release. This book will get you acquainted with the high-performance gradient-boosting LightGBM framework and show you how it can be used to solve various machine-learning problems to produce highly accurate, robust, and predictive solutions. Starting with simple machine learning models in scikit-learn, you’ll explore the intricacies of gradient boosting machines and LightGBM. You’ll be guided through various case studies to better understand the data science processes and learn how to practically apply your skills to real-world problems. As you progress, you’ll elevate your software engineering skills by learning how to build and integrate scalable machine-learning pipelines to process data, train models, and deploy them to serve secure APIs using Python tools such as FastAPI. By the end of this book, you’ll be well equipped to use various -of-the-art tools that will help you build production-ready systems, including FLAML for AutoML, PostgresML for operating ML pipelines using Postgres, high-performance distributed training and serving via Dask, and creating and running models in the Cloud with AWS Sagemaker.
Table of Contents (17 chapters)
1
Part 1: Gradient Boosting and LightGBM Fundamentals
6
Part 2: Practical Machine Learning with LightGBM
10
Part 3: Production-ready Machine Learning with LightGBM

Machine Learning Pipelines and MLOps with LightGBM

This chapter shifts the focus from data science and modeling problems to building production services for our ML solutions. We introduce the concept of machine learning pipelines, a systematic approach to processing data, and building models that ensure consistency and correctness.

We also introduce the concept of MLOps, a practice that blends DevOps and ML and addresses the need to deploy and maintain production-capable ML systems.

The chapter includes an example of building an ML pipeline using scikit-learn, encapsulating data processing, model building, and tuning. We show how to wrap the pipeline in a web API, exposing a secure endpoint for prediction. Finally, we also look at the containerization of the system and deployment to Google Cloud.

The main topics of this chapter are as follows:

  • Machine learning pipelines
  • An overview of MLOps
  • Deploying an ML pipeline for customer churn