Wyk Machine Learning with LightGBM and Python

A practitioner's guide to developing production-ready machine learning systems
1. Auflage 2024
ISBN: 978-1-80056-305-6
Verlag: De Gruyter
Format: EPUB
Kopierschutz: 0 - No protection

E-Book, Englisch, 252 Seiten

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

Autoren/Hrsg.

Wyk, Andrich van

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Programmier- und Skriptsprachen
• Mathematik | Informatik EDV | Informatik Informatik
• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Software Engineering

Inhaltsverzeichnis

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

Über Autor(innen)

Wyk Andrich van :

Andrich van Wyk has 15 years of experience in machine learning R&D and building AI-driven solutions. He also has broad experience as a software engineer and architect with over a decade of industry experience working on enterprise systems. He graduated cum laude with an M.Sc. in Computer Science from the University of Pretoria. His work focused on neural networks and population-based algorithms such as Particle Swarm Optimization and Honey-Bee Foraging. Andrich also writes about software and machine learning on his blog and his Substack. He currently resides in South Africa with his wife and daughter.