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.
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.
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.
