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Why AI/Data Science Projects Fail [electronic resource] : How to Avoid Project Pitfalls / by Joyce Weiner.

By: Weiner, Joyce [author.].
Contributor(s): SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Synthesis Lectures on Computation and Analytics: Publisher: Cham : Springer International Publishing : Imprint: Springer, 2021Edition: 1st ed. 2021.Description: XI, 65 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783031016851.Subject(s): Operations research | Mathematical optimization | Mathematical statistics -- Data processing | Operations Research and Decision Theory | Optimization | Statistics and ComputingAdditional physical formats: Printed edition:: No title; Printed edition:: No title; Printed edition:: No titleDDC classification: 658,403 Online resources: Click here to access online
Contents:
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.
In: Springer Nature eBookSummary: 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.
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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.

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.

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