Muutke küpsiste eelistusi

E-raamat: Why AI/Data Science Projects Fail: How to Avoid Project Pitfalls

Teised raamatud teemal:
  • Formaat - PDF+DRM
  • Hind: 30,86 €*
  • * hind on lõplik, st. muud allahindlused enam ei rakendu
  • Lisa ostukorvi
  • Lisa soovinimekirja
  • See e-raamat on mõeldud ainult isiklikuks kasutamiseks. E-raamatuid ei saa tagastada.
Why AI/Data Science Projects Fail: How to Avoid Project PitfallsSuurem pilt

DRM piirangud

  • Kopeerimine (copy/paste):

    ei ole lubatud

  • Printimine:

    ei ole lubatud

  • Kasutamine:

    Digitaalõiguste kaitse (DRM)
    Kirjastus on väljastanud selle e-raamatu krüpteeritud kujul, mis tähendab, et selle lugemiseks peate installeerima spetsiaalse tarkvara. Samuti peate looma endale  Adobe ID Rohkem infot siin. E-raamatut saab lugeda 1 kasutaja ning alla laadida kuni 6'de seadmesse (kõik autoriseeritud sama Adobe ID-ga).

    Vajalik tarkvara
    Mobiilsetes seadmetes (telefon või tahvelarvuti) lugemiseks peate installeerima selle tasuta rakenduse: PocketBook Reader (iOS / Android)

    PC või Mac seadmes lugemiseks peate installima Adobe Digital Editionsi (Seeon tasuta rakendus spetsiaalselt e-raamatute lugemiseks. Seda ei tohi segamini ajada Adober Reader'iga, mis tõenäoliselt on juba teie arvutisse installeeritud )

    Seda e-raamatut ei saa lugeda Amazon Kindle's. 

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.

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.
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. 
Lisainfo e-raamatute kohta Lisainfo e-raamatute kohta
Püsilink: https://www.kriso.ee/db/97830319087052e.html
Märksõnad: