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Название: Managing Machine Learning Projects: From design to deployment (Final Release)
Автор: Simon Thompson
Издательство: Manning Publications
Год: 2023
Страниц: 273
Язык: английский
Формат: pdf (true)
Размер: 11.7 MB
Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills required! Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. You’ll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The book’s strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues. Ferrying Machine Learning projects to production often feels like navigating uncharted waters. From accounting for large data resources to tracking and evaluating multiple models, machine learning technology has radically different requirements than traditional software. Never fear! This book lays out the unique practices you’ll need to ensure your projects succeed.
Автор: Simon Thompson
Издательство: Manning Publications
Год: 2023
Страниц: 273
Язык: английский
Формат: pdf (true)
Размер: 11.7 MB
Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills required! Managing Machine Learning Projects is an end-to-end guide for delivering machine learning applications on time and under budget. It lays out tools, approaches, and processes designed to handle the unique challenges of machine learning project management. You’ll follow an in-depth case study through a series of sprints and see how to put each technique into practice. The book’s strong consideration to data privacy, and community impact ensure your projects are ethical, compliant with global legislation, and avoid being exposed to failure from bias and other issues. Ferrying Machine Learning projects to production often feels like navigating uncharted waters. From accounting for large data resources to tracking and evaluating multiple models, machine learning technology has radically different requirements than traditional software. Never fear! This book lays out the unique practices you’ll need to ensure your projects succeed.