فهرست مطالب

• Cover
• Copyright
• Table of Contents
• Preface
• Chapter 1. Overview of Machine Learning Systems
• When to Use Machine Learning
• Understanding Machine Learning Systems
• Summary
• Chapter 2. Introduction to Machine Learning Systems Design
• Business and ML Objectives
• Requirements for ML Systems
• Iterative Process
• Framing ML Problems
• Mind Versus Data
• Summary
• Chapter 3. Data Engineering Fundamentals
• Data Sources
• Data Formats
• Data Models
• Data Storage Engines and Processing
• Modes of Dataflow
• Batch Processing Versus Stream Processing
• Summary
• Chapter 4. Training Data
• Sampling
• Labeling
• Class Imbalance
• Data Augmentation
• Summary
• Chapter 5. Feature Engineering
• Learned Features Versus Engineered Features
• Common Feature Engineering Operations
• Data Leakage
• Engineering Good Features
• Summary
• Chapter 6. Model Development and Offline Evaluation
• Model Development and Training
• Model Offline Evaluation
• Summary
• Chapter 7. Model Deployment and Prediction Service
• Machine Learning Deployment Myths
• Batch Prediction Versus Online Prediction
• Model Compression
• ML on the Cloud and on the Edge
• Summary
• Chapter 8. Data Distribution Shifts and Monitoring
• Causes of ML System Failures
• Data Distribution Shifts
• Monitoring and Observability
• Summary
• Chapter 9. Continual Learning and Test in Production
• Continual Learning
• Test in Production
• Summary
• Chapter 10. Infrastructure and Tooling for MLOps
• Storage and Compute
• Development Environment
• Resource Management
• ML Platform
• Build Versus Buy
• Summary
• Chapter 11. The Human Side of Machine Learning
• User Experience
• Team Structure
• Responsible AI
• Summary
• Epilogue
• Index
• About the Author
• Colophon