Overview

**TPOT in Python: A Practical Guide to Automated Machine Learning** is a hands-on resource for data scientists, machine learning engineers, and technical practitioners who want to streamline model development without sacrificing control or performance. The book introduces the core ideas behind automated machine learning, including model selection, hyperparameter optimization, and end-to-end pipeline design, while placing TPOT in the broader AutoML landscape. Readers will gain a clear understanding of why TPOT stands out as a powerful open-source framework for building effective machine learning workflows in Python. The book takes a practical, step-by-step approach to TPOT's inner workings, showing how its genetic programming engine explores, evaluates, and refines machine learning pipelines. It covers essential topics such as environment setup, data preprocessing, feature engineering, cross-validation, and pipeline optimization, alongside guidance on extending TPOT for domain-specific needs. Throughout, the emphasis is on writing reliable, reproducible, and adaptable workflows that can be used confidently in real projects. Beyond the fundamentals, **TPOT in Python: A Practical Guide to Automated Machine Learning** explores advanced topics such as pipeline interpretation, export, deployment, and integration into modern MLOps practices. The book also examines explainability, compliance, and governance considerations, making it especially relevant for production and regulated environments. With practical examples and sector-focused case studies across fields like healthcare, finance, energy, retail, and IoT, this guide helps readers apply TPOT effectively and scale AutoML solutions from experimentation to enterprise use.

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