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Machine Learning with LightGBM and Python

Machine Learning with LightGBM and Python

By : Andrich van Wyk
4.4 (8)
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Machine Learning with LightGBM and Python

Machine Learning with LightGBM and Python

4.4 (8)
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)
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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

Introducing machine learning pipelines

In Chapter 6, Solving Real-World Data Science Problems with LightGBM, we gave a detailed overview of the data science life cycle, which includes various steps to train an ML model. If we were to focus only on the steps required to train a model, given data that has already been collected, those would be as follows:

  1. Data cleaning and preparation
  2. Feature engineering
  3. Model training and tuning
  4. Model evaluation
  5. Model deployment

In previous case studies, we applied these steps manually while working through a Jupyter notebook. However, what would happen if we shifted the context to a long-term ML project? If we had to repeat the process when new data becomes available, we’d have to follow the same procedure to build a model successfully.

Similarly, when we want to use the model to score new data, we must apply the steps correctly and with the correct parameters and configuration every time.

In a sense, these...

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