Enterprise Artificial Intelligence Transformation

✍ Scribed by Rashed Haq

• Publisher: Wiley
• Year: 2020
• Tongue: English
• Leaves: 369
• Edition: 1
• Category: Library

✦ Synopsis

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

Cover
Title Page
Copyright Page
Contents
Foreword: Artificial Intelligence and the New Generation of Technology Building Blocks
Prologue: A Guide to This Book
PART I A Brief Introduction to Artificial Intelligence
Chapter 1 A Revolution in the Making
The Impact of the Four Revolutions
AI Myths and Reality
The Data and Algorithms Virtuous Cycle
The Ongoing Revolution – Why Now?
AI: Your Competitive Advantage
Chapter 2 What Is AI and How Does It Work?
The Development of Narrow AI
The First Neural Network
Machine Learning
Types of Uses for Machine Learning
Types of Machine Learning Algorithms
Supervised, Unsupervised, and Semisupervised Learning
Making Data More Useful
Semantic Reasoning
Applications of AI
PART II Artificial Intelligence in the Enterprise
Chapter 3 AI in E-Commerce and Retail
Digital Advertising
Marketing and Customer Acquisition
Cross-Selling, Up-Selling, and Loyalty
Business-to-Business Customer Intelligence
Dynamic Pricing and Supply Chain Optimization
Digital Assistants and Customer Engagement
Chapter 4 AI in Financial Services
Anti-Money Laundering
Loans and Credit Risk
Predictive Services and Advice
Algorithmic and Autonomous Trading
Investment Research and Market Insights
Automated Business Operations
Chapter 5 AI in Manufacturing and Energy
Optimized Plant Operations and Assets Maintenance
Automated Production Lifecycles
Supply Chain Optimization
Inventory Management and Distribution Logistics
Electric Power Forecasting and Demand Response
Oil Production
Energy Trading
Chapter 6 AI in Healthcare
Pharmaceutical Drug Discovery
Clinical Trials
Disease Diagnosis
Preparation for Palliative Care
Hospital Care
PART III Building Your Enterprise AI Capability
Chapter 7 Developing an AI Strategy
Goals of Connected Intelligence Systems
The Challenges of Implementing AI
AI Strategy Components
Steps to Develop an AI Strategy
Some Assembly Required
Creating an AI Center of Excellence
Building an AI Platform
Defining a Data Strategy
Moving Ahead
Chapter 8 The AI Lifecycle
Defining Use Cases
Collecting, Assessing, and Remediating Data
Data Instrumentation
Data Cleansing
Data Labeling
Feature Engineering
Selecting and Training a Model
Managing Models
Testing, Deploying, and Activating Models
Testing
Governing Model Risk
Deploying the Model
Activating the Model
Production Monitoring
Conclusion
Chapter 9 Building the Perfect AI Engine
AI Platforms versus AI Applications
What AI Platform Architectures Should Do
Some Important Considerations
Should a System Be Cloud-Enabled, Onsite at an Organization, or a Hybrid of the Two?
Should a Business Store Its Data in a Data Warehouse, a Data Lake, or a Data Marketplace?
Should a Business Use Batch or Real-Time Processing?
Should a Business Use Monolithic or Microservices Architecture?
AI Platform Architecture
Data Minder
Model Maker
Inference Activator
Performance Manager
Chapter 10 Managing Model Risk
When Algorithms Go Wrong
Mitigating Model Risk
Before Modeling
During Modeling
After Modeling
Model Risk Office
Chapter 11 Activating Organizational Capability
Aligning Stakeholders
Organizing for Scale
AI Center of Excellence
Standards and Project Governance
Community, Knowledge, and Training
Platform and AI Ecosystem
Structuring Teams for Project Execution
Managing Talent and Hiring
Data Literacy, Experimentation, and Data-Driven Decisions
Conclusion
PART IV Delving Deeper into AI Architecture and Modeling
Chapter 12 Architecture and Technical Patterns
AI Platform Architecture
Data Minder
Model Maker
Inference Activator
Performance Manager
Technical Patterns
Intelligent Virtual Assistant
Personalization and Recommendation Engines
Anomaly Detection
Ambient Sensing and Physical Control
Digital Workforce
Conclusion
Chapter 13 The AI Modeling Process
Defining the Use Case and the AI Task
Selecting the Data Needed
Setting Up the Notebook Environment and Importing Data
Cleaning and Preparing the Data
Understanding the Data Using Exploratory Data Analysis
Feature Engineering
Creating and Selecting the Optimal Model
PART V Looking Ahead
Chapter 14 The Future of Society, Work, and AI
AI and the Future of Society
AI and the Future of Work
Regulating Data and Artificial Intelligence
The Future of AI: Improving AI Technology
Reinforcement Learning
Generative Adversarial Learning
Federated Learning
Natural Language Processing
Capsule Networks
Quantum Machine Learning
And This Is Just the Beginning
Further Reading
Acknowledgments
About the Author
Index
EULA