Prospective Table of Contents (Subject to Change)

• Preface
• Chapter 1: Introduction
• Chapter 2: Data Management Principles
• Chapter 3: Fairness, Privacy, and Ethical Machine Learning Systems
• Chapter 4: What Production Engineers Need to Know About Models
• Chapter 5: Feature and Training Data
• Chapter 6: Training Systems
• Chapter 7: Serving
• Chapter 8: How Product and ML Interact
• Chapter 9: Monitoring and Observability for Models
• Chapter 10: Incident Response
• Chapter 11: Case Studies & War Stories
• Chapter 12: Evaluation and Model Quality
• Chapter 13: Continuous ML Application for Infrastructure
• Chapter 14: Hacking ML Into Your Organization