Weiner J. Why AI/Data Science Projects Fail. How to Avoid Project Pitfalls

Morgan & Claypool Publishers, 2020. — 78 p. — (Synthesis Lectures on Computation and Analytics). — ISBN: 1636390404.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.Introduction and Background.
Project Phases and Common Project Pitfalls.
Five Methods to Avoid Common 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.True PDF