Automated Machine Learning with TPOT in Python (eBook, ePUB)
The Complete Guide for Developers and Engineers
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"Automated Machine Learning with TPOT in Python" Harnessing the full potential of machine learning often requires intricate expertise, iterative model tuning, and laborious pipeline engineering. "Automated Machine Learning with TPOT in Python" presents a comprehensive, practical guide to automating these demanding tasks with TPOT, one of the leading open-source AutoML frameworks in the Python ecosystem. Opening with the motivation, theory, and evolution of AutoML, the book demystifies key concepts like automated model selection, hyperparameter optimization, and sophisticated pipeline compositi...
"Automated Machine Learning with TPOT in Python"
Harnessing the full potential of machine learning often requires intricate expertise, iterative model tuning, and laborious pipeline engineering. "Automated Machine Learning with TPOT in Python" presents a comprehensive, practical guide to automating these demanding tasks with TPOT, one of the leading open-source AutoML frameworks in the Python ecosystem. Opening with the motivation, theory, and evolution of AutoML, the book demystifies key concepts like automated model selection, hyperparameter optimization, and sophisticated pipeline composition, while offering crucial comparisons among prominent AutoML libraries and their real-world impact.
The text delves deeply into the inner workings of TPOT-including its philosophy, architectural modularity, and pioneering use of genetic programming for search and optimization-revealing how users can write, extend, and manage automated workflows tailored to their needs. Readers are skillfully guided through robust environment setup, best practices in data preprocessing, advanced pipeline optimization, and domain-driven customization, ensuring both reliability and reproducibility in projects of any scale. With dedicated chapters on pipeline interpretation, export, and integration, the book equips practitioners with the tools to transition smoothly from experimental design to production deployment, while thoughtfully addressing explainability and compliance for regulated environments.
Balancing technical rigor with practical application, the book features advanced use cases and sector-specific case studies spanning bioinformatics, energy, finance, IoT, retail, and healthcare. Through hands-on examples and lessons learned from real-world deployments, readers gain insights into scaling AutoML with distributed and cloud-native solutions, integrating TPOT into modern MLOps pipelines, and stewarding deployed models with strong governance. Whether you are a data scientist, engineer, or technical leader, this book serves as the definitive blueprint for accelerating machine learning innovation with intelligent automation.
Harnessing the full potential of machine learning often requires intricate expertise, iterative model tuning, and laborious pipeline engineering. "Automated Machine Learning with TPOT in Python" presents a comprehensive, practical guide to automating these demanding tasks with TPOT, one of the leading open-source AutoML frameworks in the Python ecosystem. Opening with the motivation, theory, and evolution of AutoML, the book demystifies key concepts like automated model selection, hyperparameter optimization, and sophisticated pipeline composition, while offering crucial comparisons among prominent AutoML libraries and their real-world impact.
The text delves deeply into the inner workings of TPOT-including its philosophy, architectural modularity, and pioneering use of genetic programming for search and optimization-revealing how users can write, extend, and manage automated workflows tailored to their needs. Readers are skillfully guided through robust environment setup, best practices in data preprocessing, advanced pipeline optimization, and domain-driven customization, ensuring both reliability and reproducibility in projects of any scale. With dedicated chapters on pipeline interpretation, export, and integration, the book equips practitioners with the tools to transition smoothly from experimental design to production deployment, while thoughtfully addressing explainability and compliance for regulated environments.
Balancing technical rigor with practical application, the book features advanced use cases and sector-specific case studies spanning bioinformatics, energy, finance, IoT, retail, and healthcare. Through hands-on examples and lessons learned from real-world deployments, readers gain insights into scaling AutoML with distributed and cloud-native solutions, integrating TPOT into modern MLOps pipelines, and stewarding deployed models with strong governance. Whether you are a data scientist, engineer, or technical leader, this book serves as the definitive blueprint for accelerating machine learning innovation with intelligent automation.
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