Buch, Englisch, 71 Seiten, Format (B × H): 168 mm x 240 mm, Gewicht: 158 g
How to Avoid Project Pitfalls
Buch, Englisch, 71 Seiten, Format (B × H): 168 mm x 240 mm, Gewicht: 158 g
Reihe: Synthesis Lectures on Computation and Analytics
ISBN: 978-3-031-90869-9
Verlag: Springer
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.
Zielgruppe
Professional/practitioner
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Optimierung
- Mathematik | Informatik EDV | Informatik Business Application Mathematische & Statistische Software
- Mathematik | Informatik Mathematik Stochastik
- Wirtschaftswissenschaften Betriebswirtschaft Management Entscheidungsfindung
- Mathematik | Informatik EDV | Informatik Angewandte Informatik
- Wirtschaftswissenschaften Betriebswirtschaft Unternehmensforschung
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
Weitere Infos & Material
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.
