Why AI/Data Science Projects Fail

Erscheinungsjahr 2020
ISBN: 978-1-63639-038-3
Verlag: Morgan & Claypool Publishers

Buch, Englisch, 77 Seiten, Paperback, Format (B × H): 191 mm x 235 mm

Reihe: Synthesis Lectures on Computation and Analytics

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.

Autoren/Hrsg.

Fachgebiete

• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Wirtschaftswissenschaften Betriebswirtschaft Management Projektmanagement
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz

Inhaltsverzeichnis

- 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

Über Autor(innen)

Joyce Weiner is a Principal Engineer at Intel Corporation. Her area of technical expertise is data science and using data to drive efficiency. Joyce is a black belt in Lean Six Sigma. She has a B.S. in Physics from Rensselaer Polytechnic Institute, and an M.S. in Optical Sciences from the University of Arizona. She lives with her husband outside Phoenix, Arizona.