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The AI-Powered Product Manager

Combining Strategy and Technology

Parámetros

  • 414 páginas
  • 15 horas de lectura

Más información sobre el libro

This comprehensive guide equips product managers with essential knowledge and tools to thrive in the rapidly evolving realms of AI and Machine Learning. It bridges traditional product management practices with the innovative techniques required for developing AI-powered products. Key areas covered include the significance of Machine Learning and AI in product management, mastering business strategy through traditional algorithms and hypothesis testing, and understanding core concepts like Deep Learning and the distinctions between Supervised, Unsupervised, and Reinforcement Learning. The book also addresses product discovery with AI, focusing on problem identification, user research methods, and prototyping. It provides insights into effective data management strategies, including data growth, open data, and crowdsourcing. Additionally, it delves into product development methodologies, prioritization, and agile practices, alongside building and evaluating high-performance AI models. Readers will learn about managing trial phases in product deployment, continuous monitoring, and feedback implementation. The guide emphasizes effective management of Data Scientists and ML Engineers, enhancing communication skills with stakeholders, and addressing privacy and bias concerns in AI. Concluding with a forward-looking exploration of product management's future, this resource is invaluable for both seasoned and new product managers se

Compra de libros

The AI-Powered Product Manager, Luis Jurado

Idioma
Publicado en
2023
Encuadernación
(Tapa blanda)
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Título
The AI-Powered Product Manager
Subtítulo
Combining Strategy and Technology
Idioma
Inglés
Editorial
Luis Jurado
Publicado en
2023
Formato
Tapa blanda
Páginas
414
ISBN10
1739400429
ISBN13
9781739400422
Serie
Descripción
This comprehensive guide equips product managers with essential knowledge and tools to thrive in the rapidly evolving realms of AI and Machine Learning. It bridges traditional product management practices with the innovative techniques required for developing AI-powered products. Key areas covered include the significance of Machine Learning and AI in product management, mastering business strategy through traditional algorithms and hypothesis testing, and understanding core concepts like Deep Learning and the distinctions between Supervised, Unsupervised, and Reinforcement Learning. The book also addresses product discovery with AI, focusing on problem identification, user research methods, and prototyping. It provides insights into effective data management strategies, including data growth, open data, and crowdsourcing. Additionally, it delves into product development methodologies, prioritization, and agile practices, alongside building and evaluating high-performance AI models. Readers will learn about managing trial phases in product deployment, continuous monitoring, and feedback implementation. The guide emphasizes effective management of Data Scientists and ML Engineers, enhancing communication skills with stakeholders, and addressing privacy and bias concerns in AI. Concluding with a forward-looking exploration of product management's future, this resource is invaluable for both seasoned and new product managers se