Thompson S. - Managing Machine Learning Projects [2023, PDF, ENG] torrent

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Managing Machine Learning Projects

Год издания: 2023
Автор: Thompson S.

Издательство: Manning
ISBN: 978-1633439023
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 273

Описание: Guide machine learning projects from design to production with the techniques in this unique project management guide. No ML skills required!
In Managing Machine Learning Projects you’ll learn essential machine learning project management techniques, including:
- Understanding an ML project’s requirements
- Setting up the infrastructure for the project and resourcing a team
- Working with clients and other stakeholders
- Dealing with data resources and bringing them into the project for use
- Handling the lifecycle of models in the project
- Managing the application of ML algorithms
- Evaluating the performance of algorithms and models
- Making decisions about which models to adopt for delivery
- Taking models through development and testing
- Integrating models with production systems to create effective applications
- Steps and behaviors for managing the ethical implications of ML technology

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.
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1. Introduction: Delivering machine learning projects is hard; let’s do it better 1
2. Pre-project: From opportunity to requirements 14
3. Pre-project: From requirements to proposal 39
4. Getting started 63
5. Diving into the problem 93
6. EDA, ethics, and baseline evaluations 117
7. Making useful models with ML 140
8. Testing and selection 163
9. Sprint 3: system building and production 189
10. Post project (sprint Ω) 220
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EPUB - https://rutracker.org/forum/viewtopic.php?t=6383616