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 Dev Ops, 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.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. 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.
表中的内容
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 Dev Ops 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.
关于作者
Eberhard Hechler is an Executive Architect at the IBM Germany R&D Lab. He is a member of the DB2 Analytics Accelerator development group and addresses the broader data and AI on IBM Z scope, including machine learning for z/OS. After two-and-a-half years at the IBM Kingston Lab in New York, he worked in software development, performance optimization, IT/solution architecture and design, open source (Hadoop and Spark) integration, and master data management. He is a member of the IBM Academy of Technology Leadership team, and co-authored the following books:
Enterprise MDM,
The Art of Enterprise Information Architecture, and
Beyond Big Data.
Martin Oberhofer is an IBM Distinguished Engineer and Executive Architect. He is a technologist and engineering leader with deep expertise in master data management, data governance, data integration, metadata and reference data management, artificial intelligence, and machine learning. He is accomplished at translating customer needs into software solutions, and works collaboratively with globally distributed development, design, and management teams. He guides development teams using Agile and Dev Ops software development methods. He is an elected member of the IBM Academy of Technology and the TEC CR. He is a certified IBM Master Inventor with over 100 granted patents and numerous publications, including four books.
Thomas Schaeck is an IBM Distinguished Engineer at IBM Data and AI, leading Watson Studio on IBM Cloud (Cloud Pak for Data) Desktop and integration with other IBM offerings. Previously, he led architecture and technical strategy for IBM Connections, Web Sphere Portal, and IBM Open Pages. He also led architecture and technical direction for Web Sphere Portal Platform and development of the Web Sphere Portal Foundation, initiated and led the portal standards Java Portlet API and OASIS WSRP and Apache open source reference implementations, and initiated and led the Web 2.0 initiative for Web Sphere Portal.
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