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Full Description
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.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.
Contents
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.
