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 …… 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
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