Foreword
Preface
1. Introduction
The ML Lifecycle
Data Collection and Analysis
ML Training Pipelines
Build and Validate Applications
Quality and Performance Evaluation
Defining and Measuring SLOs
Launch
Monitoring and Feedback Loops
Lessons from the Loop
2. Data Management Principles
Data as Liability
The Data Sensitivity of ML Pipelines
Phases of Data
Creation
Ingestion
Processing
Storage
Management
Analysis and Visualization
Data Reliability
Durability
Consistency
Version Control
Performance
Availability
Data Integrity
Security
Privacy
Policy and Compliance
Conclusion
3. Basic Introduction to Models
What Is a Model?
A Basic Model Creation Work_flow
Model Architecture Versus Model Definition Versus Trained Model
Where Are the Vulnerabilities?
Training Data
Labels
Training Methods
Infrastructure and Pipelines
Platforms
Feature Generation
Upgrades and Fixes
A Set of Useful Questions to Ask About Any Model
An Example ML System
Yarn Product Click-Prediction Model
Features
Labels for Features
Model Updating
Model Serving
Common Failures
Conclusion
4. Feature and Training Data
Features
Feature Selection and Engineering
Lifecycle of a Feature
Feature Systems
Labels
Human-Generated Labels
Annotation Workforces
Measuring Human Annotation Quality
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5. Evaluating Model Validity and Quality
6. Fairness, Privacy, and Ethical ML Systems
7. Training Systems
8. Serving
9. Monitoring and Observability for Models
10. Continuous ML
11. Incident Response
12. How Product and ML Interact
13. Integrating ML into Your Organization
14. Practical ML Org Implementation Examples
15. Case Studies: MLOps in Practice
Index