Artificial intelligence for business: a roadmap for getting started with AI
"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 likeli...
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Hoboken, New Jersey
Wiley
[2020]
|
| Schlagworte: | |
| Online-Zugang: | FHD01 |
| Zusammenfassung: | "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 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"-- |
| Beschreibung: | 1 Online-Ressource |
| ISBN: | 9781119651802 |
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| 505 | 8 | |a Introduction Ideation -- Defining the project -- Data curation and governance -- Prototyping -- Production -- Thriving with an AI lifecycle -- Conclusion.Table of Contents | |
| 520 | |a "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 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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Datensatz im Suchindex
| _version_ | 1804181459470123008 |
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| author | Anderson, Jason L. Coveyduc, Jeffrey L. |
| author_facet | Anderson, Jason L. Coveyduc, Jeffrey L. |
| author_role | aut aut |
| author_sort | Anderson, Jason L. |
| author_variant | j l a jl jla j l c jl jlc |
| building | Verbundindex |
| bvnumber | BV046719765 |
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| collection | ZDB-30-PQE |
| contents | Introduction Ideation -- Defining the project -- Data curation and governance -- Prototyping -- Production -- Thriving with an AI lifecycle -- Conclusion.Table of Contents |
| ctrlnum | (OCoLC)1155085958 (DE-599)BVBBV046719765 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Electronic eBook |
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| id | DE-604.BV046719765 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T14:33:15Z |
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| institution | BVB |
| isbn | 9781119651802 |
| language | English |
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| oclc_num | 1155085958 |
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| publisher | Wiley |
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| spelling | Anderson, Jason L. Verfasser aut Artificial intelligence for business a roadmap for getting started with AI Jeffrey L. Coveyduc, Jason L. Anderson Hoboken, New Jersey Wiley [2020] 1 Online-Ressource txt rdacontent c rdamedia cr rdacarrier Introduction Ideation -- Defining the project -- Data curation and governance -- Prototyping -- Production -- Thriving with an AI lifecycle -- Conclusion.Table of Contents "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 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"-- Artificial intelligence / Economic aspects Business enterprises / Technological innovations Artificial intelligence / Data processing Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s 1\p DE-604 Coveyduc, Jeffrey L. Verfasser aut Erscheint auch als Online-Ausgabe, adobe pdf 978-1-119-65141-3 Erscheint auch als Druck-Ausgabe, hardback 978-1-119-65173-4 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Anderson, Jason L. Coveyduc, Jeffrey L. Artificial intelligence for business a roadmap for getting started with AI Introduction Ideation -- Defining the project -- Data curation and governance -- Prototyping -- Production -- Thriving with an AI lifecycle -- Conclusion.Table of Contents Artificial intelligence / Economic aspects Business enterprises / Technological innovations Artificial intelligence / Data processing Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)4033447-8 |
| title | Artificial intelligence for business a roadmap for getting started with AI |
| title_auth | Artificial intelligence for business a roadmap for getting started with AI |
| title_exact_search | Artificial intelligence for business a roadmap for getting started with AI |
| title_exact_search_txtP | Artificial intelligence for business a roadmap for getting started with AI |
| title_full | Artificial intelligence for business a roadmap for getting started with AI Jeffrey L. Coveyduc, Jason L. Anderson |
| title_fullStr | Artificial intelligence for business a roadmap for getting started with AI Jeffrey L. Coveyduc, Jason L. Anderson |
| title_full_unstemmed | Artificial intelligence for business a roadmap for getting started with AI Jeffrey L. Coveyduc, Jason L. Anderson |
| title_short | Artificial intelligence for business |
| title_sort | artificial intelligence for business a roadmap for getting started with ai |
| title_sub | a roadmap for getting started with AI |
| topic | Artificial intelligence / Economic aspects Business enterprises / Technological innovations Artificial intelligence / Data processing Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Artificial intelligence / Economic aspects Business enterprises / Technological innovations Artificial intelligence / Data processing Künstliche Intelligenz |
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