Enterprise Artificial Intelligence Transformation
AI is everywhere. From doctor's offices to cars and even refrigerators, AI technology is quickly infiltrating our daily lives. AI has the ability to transform simple tasks into technological feats at a human level. This will change the world, plain and simple. That's why AI mastery is such a sought-after skill for tech professionals.
Author Rashed Haq is a subject matter expert on AI, having developed AI and data science strategies, platforms, and applications for Publicis Sapient's clients for over 10 years. He shares that expertise in the new book, Enterprise Artificial Intelligence Transformation.
The first of its kind, this book grants technology leaders the insight to create and scale their AI capabilities and bring their companies into the new generation of technology. As AI continues to grow into a necessary feature for many businesses, more and more leaders are interested in harnessing the technology within their own organizations. In this new book, leaders will learn to master AI fundamentals, grow their career opportunities, and gain confidence in machine learning.
Enterprise Artificial Intelligence Transformation covers a wide range of topics, including:
- Real-world AI use cases and examples
- Machine learning, deep learning, and slimantic modeling
- Risk management of AI models
- AI strategies for development and expansion
- AI Center of Excellence creating and management
If you're an industry, business, or technology professional that wants to attain the skills needed to grow your machine learning capabilities and effectively scale the work you're already doing, you'll find what you need in Enterprise Artificial Intelligence Transformation.
Table of Contents
Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks xv
Prologue: A Guide to This Book xxi
Part I: A Brief Introduction to Artificial Intelligence 1
Chapter 1: A Revolution in the Making 3
The Impact of the Four Revolutions 4
AI Myths and Reality 6
The Data and Algorithms Virtuous Cycle 7
The Ongoing Revolution - Why Now? 8
AI: Your Competitive Advantage 13
Chapter 2: What Is AI and How Does It Work? 17
The Development of Narrow AI 18
The First Neural Network 20
Machine Learning 20
Types of Uses for Machine Learning 23
Types of Machine Learning Algorithms 24
Supervised, Unsupervised, and Semisupervised Learning 28
Making Data More Useful 32
Semantic Reasoning 34
Applications of AI 40
Part II: Artificial Intelligence In the Enterprise 43
Chapter 3: AI in E-Commerce and Retail 45
Digital Advertising 46
Marketing and Customer Acquisition 48
Cross-Selling, Up-Selling, and Loyalty 52
Business-to-Business Customer Intelligence 55
Dynamic Pricing and Supply Chain Optimization 57
Digital Assistants and Customer Engagement 59
Chapter 4: AI in Financial Services 67
Anti-Money Laundering 68
Loans and Credit Risk 71
Predictive Services and Advice 72
Algorithmic and Autonomous Trading 75
Investment Research and Market Insights 77
Automated Business Operations 81
Chapter 5: AI in Manufacturing and Energy 85
Optimized Plant Operations and Assets Maintenance 88
Automated Production Lifecycles 91
Supply Chain Optimization 91
Inventory Management and Distribution Logistics 93
Electric Power Forecasting and Demand Response 94
Oil Production 96
Energy Trading 99
Chapter 6: AI in Healthcare 103
Pharmaceutical Drug Discovery 104
Clinical Trials 105
Disease Diagnosis 106
Preparation for Palliative Care 109
Hospital Care 111
PART III: BUILDING YOUR ENTERPRISE AI CAPABILITY 117
Chapter 7: Developing an AI Strategy 119
Goals of Connected Intelligence Systems 120
The Challenges of Implementing AI 122
AI Strategy Components 126
Steps to Develop an AI Strategy 127
Some Assembly Required 129
Creating an AI Center of Excellence 130
Building an AI Platform 131
Defining a Data Strategy 132
Moving Ahead 134
Chapter 8: The AI Lifecycle 137
Defining Use Cases 138
Collecting, Assessing, and Remediating Data 143
Data Instrumentation 144
Data Cleansing 145
Data Labeling 146
Feature Engineering 148
Selecting and Training a Model 151
Managing Models 160
Testing, Deploying, and Activating Models 164
Testing 164
Governing Model Risk 165
Deploying the Model 166
Activating the Model 166
Production Monitoring 168
Conclusion 169
Chapter 9: Building the Perfect AI Engine 171
AI Platforms versus AI Applications 172
What AI Platform Architectures Should Do 172
Some Important Considerations 179
Should a System Be Cloud-Enabled, Onsite at an Organization, or a Hybrid of the Two? 179
Should a Business Store Its Data in a Data Warehouse, a Data Lake, or a Data Marketplace? 180
Should a Business Use Batch or Real-Time Processing? 182
Should a Business Use Monolithic or Microservices Architecture? 184
AI Platform Architecture 186
Data Minder 186
Model Maker 187
Inference Activator 188
Performance Manager 190
Chapter 10: Managing Model Risk 193
When Algorithms Go Wrong 195
Mitigating Model Risk 197
Before Modeling 197
During Modeling 199
After Modeling 201
Model Risk Office 209
Chapter 11: Activating Organizational Capability 213
Aligning Stakeholders 214
Organizing for Scale 215
AI Center of Excellence 217
Standards and Project Governance 218
Community, Knowledge, and Training 220
Platform and AI Ecosystem 221
Structuring Teams for Project Execution 222
Managing Talent and Hiring 225
Data Literacy, Experimentation, and Data-Driven Decisions 228
Conclusion 230
Part IV: Delving Deeper Into AI Architecture and Modeling 233
Chapter 12: Architecture and Technical Patterns 235
AI Platform Architecture 236
Data Minder 236
Model Maker 239
Inference Activator 242
Performance Manager 244
Technical Patterns 244
Intelligent Virtual Assistant 244
Personalization and Recommendation Engines 247
Anomaly Detection 250
Ambient Sensing and Physical Control 251
Digital Workforce 255
Conclusion 257
Chapter 13: The AI Modeling Process 259
Defining the Use Case and the AI Task 260
Selecting the Data Needed 262
Setting Up the Notebook Environment and Importing Data 264
Cleaning and Preparing the Data 265
Understanding the Data Using Exploratory Data Analysis 268
Feature Engineering 274
Creating and Selecting the Optimal Model 277
Part V: Looking Ahead 289
Chapter 14: The Future of Society, Work, and AI 291
AI and the Future of Society 292
AI and the Future of Work 294
Regulating Data and Artificial Intelligence 296
The Future of AI: Improving AI Technology 300
Reinforcement Learning 300
Generative Adversarial Learning 302
Federated Learning 303
Natural Language Processing 304
Capsule Networks 305
Quantum Machine Learning 306
And This Is Just the Beginning 307
Further Reading 313
Acknowledgments 317
About the Author 319
Index 321
