Why AI/Data Science Projects Fail

This Second Edition addresses five common pitfalls that prevent projects from reaching deployment and provides tools and methods to avoid these pitfalls. Current statistics show that 87% of AI and Big Data projects fail by never reaching deployment, making this book an essential resource for data science and AI practitioners, as well as managers. The author illustrates the methods and tools by including real examples from her experience building and deploying data science and AI projects. This new edition builds upon the original book with revisions, updates and features a new chapter on Generative AI.

Presents an effective framework for building successful AI and data science projects from the ground up Includes strategies, tools, real-world examples, and detailed templates that readers can apply to their own projects Provides tips specifically aimed toward managers of data science and AI teams

Autorentext

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.

Inhalt

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.- Considerations for Generative AI Projects in the Enterprise.- Summary of the Five Methods to Avoid Common Pitfalls.