Deploying AI in the Enterprise : IT Approaches for Design, DevOps, Governance, Change Management, Blockchain, and Quantum Computing

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. Table of Contents 5 About the Authors 13 About the Technical Reviewer 15 Foreword 16 Acknowledgments 19 Book Layout 20 Part I: Getting Started 24 Chapter 1: AI Introduction 25 AI for the Enterprise 25 AI Objective: Automated Actions 27 Actions Require Decisions 27 Decisions Require Predictions 28 Smart Decisions: Prediction and Optimization 30 Data Fuels AI 31 Garbage In, Garbage Out 31 Bias 32 Information Architecture for AI 32 Putting It Together: The AI Life Cycle 34 Understanding Use Case and Feasibility 35 Collect Data 35 Explore and Understand Data 35 Prepare and If Needed Label Data 36 Extract Features 37 Train and Validate Models 37 Model Reviews and Approvals 38 Deploying and Monitoring Models in Production 38 Predictions for Applications or Processes 39 Optimize Actions 40 Reap the Benefits of Automated Actions 41 AI and Cognitive Computing 41 AI, Blockchain, Quantum Computing 42 Key Takeaways 42 References 43 Chapter 2: AI Historical Perspective 45 Introduction 46 Historical Perspective 46 Technological Advancements 47 The Evolution of AI 49 Some Industry Examples 50 Key Takeaways 53 References 54 Chapter 3: Key ML, DL, and DO Concepts 56 Machine Learning (ML) 56 Types of ML 58 Types of ML Algorithms 59 Regression and Classification 60 Decision Trees 60 Clustering 61 Bayesian 61 Dimensionality Reduction 61 Auto AI 63 Toward AI Model Eminence 66 Deep Learning (DL) 66 What Is DL? 66 Artificial Neural Networks (ANNs) 67 Deep Learning Networks (DLNs) 68 Decision Optimization (DO) 69 Key Takeaways 70 References 72 Part II: AI Deployment 74 Chapter 4: AI Information Architecture 75 Information Architecture – A Short Review 76 Terminology and Definitions 77 Methods and Models 79 Enterprise Suitability of AI 80 Relevance of Information Architecture for AI 80 Information Architecture in the Context of AI 85 AI Information Architecture and the ML Workflow 89 AI Information Architecture for Any Cloud 92 Information Architecture for a Trusted AI Foundation 94 Data Discovery and Trustworthiness of Data 95 Data Transformation and Synchronization 95 Data Exploration to Gain Relevant Insight 97 Data Provisioning for Relevant and Timely Inference 97 Role of Master Data Management (MDM) for AI 98 Mapping to Sample Vendor Offerings 98 IBM Cloud Pak for Data 99 High-Level Description 99 IBM and Third-Party Add-ons 101 Data Virtualization 102 Amazon 103 Microsoft 104 Google 105 Sample Scenarios 107 Manage Enterprise Data Anywhere 108 Operationalizing Data Science and AI 108 Maintain Accuracy of DL and ML Models 109 Explore Data to Gain Insight 109 Key Takeaways 110 References 111 Chapter 5: From Data to Predictions to Optimal Actions 114 Use Case: A Marketing Campaign 114 Naïve Solution: ML 101 115 Refined Solution: ML plus DO 116 Example: ML plus DO 116 Create a Project 117 Connect Data 117 Refine, Visualize, Analyze Data 118 Create and Train Predictive Models 120 Auto AI 120 SPSS Flows 123 Notebooks 123 Deploy ML Models 126 Create DO Models 127 Deploy DO Models 130 Taking ML and DO Models to Production 130 Embedding AI in Applications and Processes 131 Key Takeaways 131 References 133 Chapter 6: The Operationalization of AI 134 Introduction 134 Challenges of AI Operationalization 135 General Aspects of AI Operationalization 139 Deployment Aspects 141 Platform Interoperability 142 Vendor Transparency 142 Key AI Operationalization Domains 143 Influencing Characteristics 144 Data Engineering and Pipelining 146 Integrated Scoring Services 148 Inference of Insight 150 AI Model Monitoring 151 Analyzing Results and Errors 154 AI Model Adaptations 156 Key Takeaways 157 References 158 Chapter 7: Design Thinking and DevOps in the AI Context 160 Introduction 160 Design Thinking and DevOps Revisited 161 Traditional Design Thinking 161 Traditional DevOps 163 Benefit of Design Thinking and DevOps 165 Design Thinking in the Context of AI 166 AI Influence on Design Thinking 166 Challenges for Design Thinking 167 DevOps in the Context of AI 168 AI Influence on DevOps 169 Challenges for DevOps 171 Key Aspects of AI Design Thinking 172 AI Design Thinking Model 172 AI Design Thinking Value 174 Key Aspects of AI DevOps 175 AI DevOps Model 175 AI DevOps Value 177 Key Takeaways 177 References 178 Part III: AI in Context 181 Chapter 8: AI and Governance 182 Scope of Governance 183 Governance – A Short Review 183 Data and Information Governance 186 Infusing AI into Data Governance 198 AI Applied to Metadata and Data Quality Management 198 AI Applied to Data Security 205 Governance in the Context of AI 207 Beyond Traditional Information Governance 208 Challenges for AI Governance 209 Regulations Driving AI Governance 211 Key Aspects of AI Governance 212 Rules and Policies 213 Glossaries 214 Search and Discovery 215 Classification 215 Provenance and Lineage 216 Mapping to Sample Vendor Offerings 217 Amazon Web Services (AWS) 218 Microsoft AI Principles 219 IBM Offerings 220 Key Takeaways 221 References 223 Chapter 9: Applying AI to Master Data Management 229 Introduction to Master Data Management 230 Digital Twin and Customer Data Platform 232 Infusing AI into Master Data Management 234 Operationalizing Customer Insight via MDM 245 Key Takeaways 248 References 249 Chapter 10: AI and Change Management 251 Introduction 251 Scope of Change Management 252 Change Management – Scope and Definition 252 Traditional Change Management 254 Change Management in the Context of AI 254 AI Influence on Change Management 255 Challenges for Change Management 257 Driving Change on Organizational Structures 258 Key Aspects of AI Change Management 259 AI Change Management Framework 260 AI for IT Change Management 262 Social Media Analytics to Optimize Changes 265 Key Takeaways 266 References 267 Chapter 11: AI and Blockchain 269 Blockchain for the Enterprise 272 Introduction to the Hyperledger Blockchain 273 Tradelens Uses Hyperledger Blockchain 274 On-Chain vs. Off-Chain Analytics 275 Existing Technology Adopting Blockchain Concepts 279 Using Blockchain for AI Governance 281 Key Takeaways 284 References 285 Chapter 12: AI and Quantum Computing 288 What Is a Quantum Computer? 288 Superposition 291 Entanglement 294 Quantum Computer 295 Shor’s Algorithm 296 AI and Quantum Computing Today 299 AI and Quantum Computing Tomorrow 304 Key Takeaways 306 References 307 Part IV: AI Limitations and Future Challenges 311 Chapter 13: Limitations of AI 312 Introduction 312 AI and the Human Brain 314 Current AI Limitations 316 Labeling and Annotation 316 Autonomous ML and DL 317 Multitask Learning 317 Explainability of Decisions 318 Insoluble Challenges 319 Cognitive Capabilities 319 Weird Situations 320 Generalization of Learning 321 Additional Research Topics 321 Key Takeaways 322 References 323 Chapter 14: In Summary and Onward 326 AI for the Enterprise – Low Hanging Fruit 326 The AI Enterprise – Whitespace 327 An Example 328 Future of AI 329 Chapter 15: Abbreviations 330 Index 335 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 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. 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 DevOps 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, WebSphere Portal, and IBM OpenPages. He also led architecture and technical direction for WebSphere Portal Platform and development of the WebSphere 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 WebSphere Portal 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. Front Matter ....Pages i-xxvi Front Matter ....Pages 1-1 AI Introduction (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 3-22 AI Historical Perspective (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 23-33 Key ML, DL, and DO Concepts (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 35-52 Front Matter ....Pages 53-53 AI Information Architecture (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 55-93 From Data to Predictions to Optimal Actions (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 95-114 The Operationalization of AI (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 115-140 Design Thinking and DevOps in the AI Context (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 141-161 Front Matter ....Pages 163-163 AI and Governance (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 165-211 Applying AI to Master Data Management (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 213-234 AI and Change Management (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 235-252 AI and Blockchain (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 253-271 AI and Quantum Computing (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 273-295 Front Matter ....Pages 297-297 Limitations of AI (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 299-312 In Summary and Onward (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 313-316 Abbreviations (Eberhard Hechler, Martin Oberhofer, Thomas Schaeck)....Pages 317-321 Back Matter ....Pages 323-331 "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."--Page 4 of cover