Machine Learning with LightGBM and Python: A Practitioner’s Guide to Developing Production-Ready Machine Learning Systems is tailored for a broad spectrum of readers passionate about harnessing data’s power through ML. The target audience for this book includes the following:

• Beginners in ML: Individuals just stepping into the world of ML will find this book immensely beneficial. It starts with foundational ML principles and introduces them to gradient boosting using LightGBM, making it an excellent entry point for newcomers.
• Experienced data scientists and ML practitioners: For those who are already familiar with the landscape of ML but want to deepen their knowledge of LightGBM and/or MLOps, this book offers advanced insights, techniques, and practical applications.
• Software engineers and architects looking to learn more about data science: Software professionals keen on transitioning to data science or integrating ML into their applications will find this book valuable. The book approaches ML theoretically and practically, emphasizing hands-on coding and real-world applications.
• MLOps engineers and DevOps professionals: Individuals working in the field of MLOps or those who wish to understand the deployment, scaling, and monitoring of ML models in production environments will benefit from the chapters dedicated to MLOps, pipelines, and deployment strategies.
• Academicians and students: Faculty members teaching ML, data science, or related courses, as well as students pursuing these fields, will find this book to be both an informative textbook and a practical guide.

Knowledge of how to program Python is necessary. Familiarity with Jupyter notebooks and Python environments is a bonus. No prior knowledge of ML is required.

In essence, anyone with a penchant for data, a background in Python programming, and an eagerness to explore the multifaceted world of ML using LightGBM will find this book a valuable addition to their repertoire.