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:
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 state-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:
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.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
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.
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
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