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Record Nr. |
UNINA9910794161203321 |
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Autore |
Coveyduc Jeffrey L |
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Titolo |
Artificial intelligence for business : a roadmap for getting started with AI / / Jeffrey L Coveyduc, Jason L Anderson |
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Pubbl/distr/stampa |
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Hoboken, New Jersey : , : Wiley, , [2020] |
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©2020 |
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ISBN |
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1-119-65141-7 |
1-119-65180-8 |
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Descrizione fisica |
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1 online resource (xi, 224 pages) : illustrations |
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Disciplina |
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Soggetti |
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Artificial intelligence - Economic aspects |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Sommario/riassunto |
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"This book will provide the reader with an easy to understand roadmap for how to take an organization through the adoption of AI technology. It will first help with the identification of which business problems and opportunities are right for AI and how to prioritize them to maximize the likelihood of success. Specific methodologies are introduced to help with finding critical training data within an organization and how to fill data gaps if they exist. With data in hand, a scoped prototype can be built to limit risk and provide tangible value to the organization as a whole to justify further investment. Finally, a production level AI system can be developed with best practices to ensure quality with not only the application code, but also the AI models. Finally with this particular AI adoption journey at an end, the authors will show that there is additional value to be gained by iterating on this AI adoption lifecycle and improving other parts of the organization. This book provides the following benefits: Organizations know they need to leverage AI but they need the described proven roadmap to enable this journey. This book identifies common pitfalls that businesses run into when adopting AI and describes how to avoid them. Enables organizations to get a handle on their data (one of their most valuable assets) which is typically not well organized and scattered throughout different parts of |
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the business. Describes, at a high level, how to build and manage AI models which is different than traditional application code practices. Covers the challenges and best practices of using AI at scale in a production environment. Applies automated testing methodologies to AI models to ensure quality improves with each iteration"-- |
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