000			03236cam a22004098i 4500
001			21422847
003			JO-AjAnu
005			20231127102348.0
008			200207s2020 nju 001 0 eng
010			_a 2020004359
020			_a9781119651734 _q(hardback)
020			_z9781119651413 _q(adobe pdf)
020			_z9781119651802 _q(epub)
040			_aDLC _beng _erda _cDLC _dJO-AjAnu
042			_apcc
050	0	0	_aHC79.I55 _b.A527 2020
082	0	0	_a006.3068 _223
100	1		_aAnderson, Jason L, _eauthor.
245	1	0	_aArtificial intelligence for business : _ba roadmap for getting started with AI / _cJason L Anderson, Jeffrey L Coveyduc.
250			_aFirst edition.
263			_a2004
264		1	_aHoboken : _bWiley, _c2020.
300			_apages cm
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
500			_aIncludes index.
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"-- _cProvided by publisher.
650		0	_aArtificial intelligence _xEconomic aspects.
650		0	_aBusiness enterprises _xTechnological innovations.
650		0	_aArtificial intelligence _xData processing.
700	1		_aCoveyduc, Jeffrey L, _eauthor.
776	0	8	_iOnline version: _aAnderson, Jason L, _tArtificial intelligence for business _bFirst edition. _dHoboken : Wiley, 2020. _z9781119651413 _w(DLC) 2020004360
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK _n0
999			_c33150 _d33150