Deploying AI in the enterprise IT approaches for design, DevOps, governance, change management, blockchain, and quantum computing

It is not an introduction, but is for the reader who is looking for examples on how to leverage data to derive actionable insight and predictions, and needs to understand and factor in the current risks and limitations of AI and what it means in an industry-relevant context

Bibliographic Details
Main Authors: Hechler, Eberhard, Oberhofer, Martin (Author), Schaeck, Thomas (Author)
Other Authors: Thummalapalli, Srinivas (writer of foreword)
Format: eBook
Language:English
Published: [Berkeley, California] Apress 2020
Subjects:
Online Access:
Collection: O'Reilly - Collection details see MPG.ReNa
LEADER 05695nmm a2200757 u 4500
001 EB001917046
003 EBX01000000000000001079948
005 00000000000000.0
007 cr|||||||||||||||||||||
008 210123 ||| eng
020 |a 9781484262061 
050 4 |a Q335 
100 1 |a Hechler, Eberhard 
245 0 0 |a Deploying AI in the enterprise  |b IT approaches for design, DevOps, governance, change management, blockchain, and quantum computing  |c Eberhard Hechler, Martin Oberhofer, Thomas Schaeck ; foreword by Srinivas Thummalapalli 
246 3 1 |a Deploying artifictial intelligence in the enterprise 
260 |a [Berkeley, California]  |b Apress  |c 2020 
300 |a xxvi, 331 pages  |b illustrations 
505 0 |a Includes bibliographical references and index 
505 0 |a Part I: Getting Started -- Chapter 1: AI Introduction -- Chapter 2: AI Historical Perspective -- Chapter 3: Key ML, DL and Decision Optimization Concepts -- Part II: AI Deployment -- Chapter 4: AI Information Architecture -- Chapter 5: From Data to Predictions to Optimal Actions -- Chapter 6: The Operationalization of AI -- Chapter 7: Design Thinking and DevOps in the AI Context -- Part III: AI in Context -- Chapter 8: Applying AI to Data Governance and MDM -- Chapter 9: AI and Governance -- Chapter 10: AI and Change Management -- Chapter 11: AI and Blockchain -- Chapter 12: AI and Quantum Computing -- Part IV: AI Limitations and Future Challenges -- Chapter 13: Limitations of AI -- Chapter 14: In Summary and Onward -- Chapter 15: Appendix: Abbreviations 
653 |a Security / bisacsh 
653 |a Computer software / fast 
653 |a Machine Learning 
653 |a SCIENCE. / bisacsh 
653 |a Logiciels 
653 |a COMPUTERS. / bisacsh 
653 |a Artificial intelligence / fast 
653 |a Software 
653 |a Machine learning / http://id.loc.gov/authorities/subjects/sh85079324 
653 |a Cryptography / bisacsh 
653 |a artificial intelligence / aat 
653 |a Quantum computers / fast 
653 |a Quantum Theory / bisacsh 
653 |a Apprentissage automatique 
653 |a software / aat 
653 |a Ordinateurs quantiques 
653 |a Databases / bisacsh 
653 |a MATHEMATICS. / bisacsh 
653 |a Physics / bisacsh 
653 |a Quantum computers / http://id.loc.gov/authorities/subjects/sh98002795 
653 |a Probability & Statistics / bisacsh 
653 |a Artificial intelligence / http://id.loc.gov/authorities/subjects/sh85008180 
653 |a Intelligence artificielle 
653 |a Artificial Intelligence 
653 |a Machine learning / fast 
653 |a Intelligence (AI) & Semantics / bisacsh 
653 |a Computer software / http://id.loc.gov/authorities/subjects/sh85029534 
653 |a Discrete Mathematics / bisacsh 
700 1 |a Oberhofer, Martin  |e author 
700 1 |a Schaeck, Thomas  |e author 
700 1 |a Thummalapalli, Srinivas  |e writer of foreword 
041 0 7 |a eng  |2 ISO 639-2 
989 |b OREILLY  |a O'Reilly 
028 5 0 |a 10.1007/978-1-4842-6206-1 
015 |a GBC0K0405 
776 |z 9781484262054 
776 |z 9781484262061 
776 |z 1484262069 
776 |z 1484262050 
856 4 0 |u https://learning.oreilly.com/library/view/~/9781484262061/?ar  |x Verlag  |3 Volltext 
082 0 |a 500 
082 0 |a 006.3 
082 0 |a 519.5 
082 0 |a 510 
520 |a It is not an introduction, but is for the reader who is looking for examples on how to leverage data to derive actionable insight and predictions, and needs to understand and factor in the current risks and limitations of AI and what it means in an industry-relevant context 
520 |a And you will come away with an understanding of how to effectively leverage AI to augment usage of core information in Master Data Management (MDM) solutions. What You Will Learn Understand the most important AI concepts, including machine learning and deep learning Follow best practices and methods to successfully deploy and operationalize AI solutions Identify critical components of AI information architecture and the importance of having a plan Integrate AI into existing initiatives within an organization Recognize current limitations of AI, and how this could impact your business Build awareness about important and timely AI research Adjust your mindset to consider AI from a holistic standpoint Get acquainted with AI opportunities that exist in various industries Who This Book Is For IT pros, data scientists, and architects who need to address deployment and operational challenges related to AI and need a comprehensive overview on how AI impacts other business critical areas.  
520 |a Your company has committed to AI. Congratulations, now what? This practical book offers a holistic plan for implementing AI from the perspective of IT and IT operations in the enterprise. You will learn about AI's capabilities, potential, limitations, and challenges. This book teaches you about the role of AI in the context of well-established areas, such as design thinking and DevOps, governance and change management, blockchain, and quantum computing, and discusses the convergence of AI in these key areas of the enterprise. Deploying AI in the Enterprise provides guidance and methods to effectively deploy and operationalize sustainable AI solutions. You will learn about deployment challenges, such as AI operationalization issues and roadblocks when it comes to turning insight into actionable predictions. You also will learn how to recognize the key components of AI information architecture, and its role in enabling successful and sustainable AI deployments.