Enterprise AIOps

Preface
	Acknowledgments
1. Demystifying AI
	AI Pilot-to-Production Challenges
		Scalability
		Sustainability
		Coordination
2. Defining the AIOps Framework
	Why You Need an AIOps Framework
	What Are the Benefits?
3. Responsible AI
	What Is Responsible AI?
	Adopting Responsible AI
	Ethics
	Workforce Development
	AI Risks and Complexities
	Risk Management Processes
4. Data: Your Most Precious Asset
	Data’s Role in AIOps
	Data Strategy
	DataOps: Operationalizing Your Data Strategy
	Data Preparation Activities
		Ingest
		Transformation
		Validation
		AI Data Governance
5. Machine Learning (ML)
	What Is an ML Model?
	ML Methodologies
	ML Advanced Topics
	ML Life Cycle
		Business Analysis
		Model Development
		Model Vetting
		Model Operation
	Machine Learning Operations (MLOps)
		Scalable Training Infrastructure
		Model Optimization Infrastructure
		Model Deployment Infrastructure
6. The Road to AI Adoption
	AI Adoption Blueprint
	Establishing Clear Objectives
	Measuring Outcomes and Performance
	Reference Architectures
		Technical Infrastructure
		Development Processes
	AIOps Integration
		Operational Components
		Component Integration
Conclusion