Why AI/Data Science Projects Fail : How to Avoid Project Pitfalls
Weiner, Joyce
Why AI/Data Science Projects Fail : How to Avoid Project Pitfalls by Joyce Weiner - 1st edition 2021 - 1 recurso en línea (XI, 65 páginas) - Synthesis Lectures on Computation and Analytics 2766-8967 .
Preface -- Introduction and Background -- Project Phases and Common Project Pitfalls -- Define Phase -- Making the Business Case: Assigning Value to Your Project -- Acquisition and Exploration of Data Phase -- Model-Building Phase -- Interpret and Communicate Phase -- Deployment Phase -- Summary of the five Methods to Avoid Common Pitfalls -- References -- Author Biography.
Recent data shows that 87% of Artificial Intelligence/Big Data projects don't make it into production (VB Staff, 2019), meaning that most projects are never deployed. This book addresses five common pitfalls that prevent projects from reaching deployment and provides tools and methods to avoid those pitfalls. Along the way, stories from actual experience in building and deploying data science projects are shared to illustrate the methods and tools. While the book is primarily for data science practitioners, information for managers of data science practitioners is included in the Tips for Managers sections.
9783031016851
10.1007/978-3-031-01685-1 doi
Gestión de proyectos
Datos masivos
Inteligencia artificial
Q335 / 2021 EB
Why AI/Data Science Projects Fail : How to Avoid Project Pitfalls by Joyce Weiner - 1st edition 2021 - 1 recurso en línea (XI, 65 páginas) - Synthesis Lectures on Computation and Analytics 2766-8967 .
Preface -- Introduction and Background -- Project Phases and Common Project Pitfalls -- Define Phase -- Making the Business Case: Assigning Value to Your Project -- Acquisition and Exploration of Data Phase -- Model-Building Phase -- Interpret and Communicate Phase -- Deployment Phase -- Summary of the five Methods to Avoid Common Pitfalls -- References -- Author Biography.
Recent data shows that 87% of Artificial Intelligence/Big Data projects don't make it into production (VB Staff, 2019), meaning that most projects are never deployed. This book addresses five common pitfalls that prevent projects from reaching deployment and provides tools and methods to avoid those pitfalls. Along the way, stories from actual experience in building and deploying data science projects are shared to illustrate the methods and tools. While the book is primarily for data science practitioners, information for managers of data science practitioners is included in the Tips for Managers sections.
9783031016851
10.1007/978-3-031-01685-1 doi
Gestión de proyectos
Datos masivos
Inteligencia artificial
Q335 / 2021 EB