Managing AI in the Enterprise: Succeeding with AI Projects and MLOps to Build Sustainable AI Organizations

Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: Why Organizations Invest in AI
	The Role of AI in Data-Driven Companies
	Calculating the Business Value of AI
	Use Case: Sales Operations Efficiency
		How and Why Up-Selling, Cross-Selling, Churn Use Cases Thrive
		Hurdles and Prerequisites for Success
		Organizational Impact
	Insights for Product Strategies
		Identifying the Business Challenge
		AI and Analytics vs. Excel
		Understanding the AI Project Deliverables and Methodology
	AI-Driven Innovation in Fashion
	Business Intelligence and AI
	Summary
Chapter 2: Structuring and Delivering AI Projects
	The Four Layers of Innovation
	Scoping AI Projects
		Understanding the Business Goal
		Understand the Insights Category
		“Prediction Only” vs. “Prediction and Explanation”
		The Training Set Challenge
		Model Update and Usage Frequency
		Identify the Suitable AI Layer
		From Scoping Questions to Business Cases
	Understanding AI Models
		Statistics-Based Models
		Neural Networks
		Advanced Neural Network Topologies
	Developing AI Models
		The Jupyter Notebook Phenomenon
		CRISP-DM for Model Development
		Improving the Productivity of Data Scientists
	Integrating AI Models in IT Solutions
	Summary
Chapter 3: Quality Assurance in and for AI
	AI Model Quality Metrics
		Performance Metrics for Classification
		Classification and Scoring
		Additional Performance Metrics
	QA Stages in AI Model Engineering
		Perfect but Worthless Model Metrics
		The Training, Validation, and Test Data Split
		Assessing the AI Model with the Training Dataset
		Assessing the AI Model with the Validation Dataset
		Assessing the AI Model with the Test Dataset
	Monitoring AI Models in Production
	Data Quality
		Technical Correctness
		Data Matches Reality?
		Reputation of Data
	QA for AI-Driven Solutions
	Summary
Chapter 4: Ethics, Regulations, and Explainability
	AI Ethics
		The Three Areas of Ethical Risks
		Handling Ethical Dilemmas
		On Ethical AI Models
		AI Ethics Governance
	AI and Regulations
		Data Privacy Laws: The GDPR Example
		The EU’s “AI Act” Proposal
		The Federal Trade Commission’s Approach in the US
	Explainable AI
		Scenarios for XAI
		Local Explainability
		Global Explainability
	Summary
Chapter 5: Building an AI Delivery Organization
	Shaping an AI Service
		IT Services Characteristics
		AI Service Types
		Characterizing AI Service Types
		Understanding Service Attributes
		Designing (for) and Measuring Service Quality
	Managing AI Project Services
		The Capabilities Triumvirate for AI Project Services
		Workload Pattern
		Budgets and Costs
		Selling Results: Data Story Telling
	Managing AI Operations Services
		The Six AI Capabilities
		Workload Pattern
		Understanding and Managing Costs Drivers
		Model Management
		Organizing an AI Organization
	Summary
Chapter 6: AI and Data Management Architectures
	Architecting AI Environments
		Ingestion Data into AI Environments
		Storing Training Data
		Data Lakes vs. Data Warehouses
		Data Catalogs
		Model and Code Repositories
		Executing AI Models
	AI and Data Management Architectures
		AI and Classic Data Warehouse Architectures
		Self-Service Business Intelligence
		Pantheistic Intelligence
		New Data Categories
	Cloud Services and AI Architecture
	Summary
Chapter 7: Securing and Protecting AI Environments
	The CIA Triangle
	Security-Related Responsibilities
	Mapping the Risk Landscape
		Threat Actors
		Assets in AI Organizations
		Confidentiality Threats
		Integrity Threats
		Availability Threats
		From Threats to Risks and Mitigation
	Securing AI-Related Systems
		System Hardening
		Governance
		Data Compartmentalization and Access Management
		Advanced Techniques for Sensitive Attributes
		Probing Detection
		Cloud-AI Risk Mitigation
		The ISO 27000 Information Security Standard
		Summary
Chapter 8: Looking Forward
Index