Machine Learning with LightGBM and Python: A practitioner's guide to developing production-ready ML systems

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Take your software to the next level and solve real-world data science problems by building production-ready machine learning solutions using LightGBM and Python

Key Features
Get started with LightGBM, a powerful gradient-boosting library for building ML solutions
Apply data science processes to real-world problems through case studies
Elevate your software by building machine learning solutions on scalable platforms
Purchase of the print or Kindle book includes a free PDF eBook

Book Description
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.
What you will learn
Get an overview of ML and working with data and models in Python using scikit-learn
Explore decision trees, ensemble learning, gradient boosting, DART, and GOSS
Master LightGBM and apply it to classification and regression problems
Tune and train your models using AutoML with FLAML and Optuna
Build ML pipelines in Python to train and deploy models with secure and performant APIs
Scale your solutions to production readiness with AWS Sagemaker, PostgresML, and Dask

Who this book is for
This book is for software engineers aspiring to be better machine learning engineers and data scientists unfamiliar with LightGBM, looking to gain in-depth knowledge of its libraries. Basic to intermediate Python programming knowledge is required to get started with the book. The book is also an excellent source for ML veterans, with a strong focus on ML engineering with up-to-date and thorough coverage of platforms such as AWS Sagemaker, PostgresML, and Dask.

Table of Contents
An Introduction Machine Learning and Decision Trees
Decision Tree Ensembles: Bagging and Boosting
An Overview of LightGBM in Python
LightGBM, XGBoost and Deep Learning
LightGBM Parameter Optimization and Tuning with Optuna
Solving Real World Problems with LightGBM
LightGBM AutoML with FLAML
Machine Learning Pipelines with LightGBM
Deploying LightGBM to AWS SageMaker
Deploying LightGBM with PostgresML
Distributed Training and Serving of LightGBM using Dask

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