Chapter 1. Human-centric infrastructure
Chapter 1. Introducing data science infrastructure
Chapter 1. Summary
Chapter 1. What is data science infrastructure
Chapter 1. Why good infrastructure matters
Chapter 2. Introducing workflows
Chapter 2. Summary
Chapter 2. The toolchain of data science
Chapter 3. Branching and merging
Chapter 3. Introducing Metaflow
Chapter 3. Metaflow in Action
Chapter 3. Summary
Chapter 4. Handling failures
Chapter 4. Scaling with the compute layer
Chapter 4. Summary
Chapter 4. The compute layer
Chapter 4. The compute layer in Metaflow
Chapter 5. Practicing horizontal scalability
Chapter 5. Practicing performance optimization
Chapter 5. Practicing scalability and performance
Chapter 5. Summary
Chapter 6. Going to production
Chapter 6. Stable execution environments
Chapter 6. Stable operations
Chapter 6. Summary
Chapter 7. From data to features
Chapter 7. Interfacing with data infrastructure
Chapter 7. Processing data
Chapter 7. Summary
Chapter 8. Summary
Chapter 8. Using and operating models
Chapter 9. Deep regression model
Chapter 9. Machine learning with the full stack
Chapter 9. Summarizing lessons learned
Chapter 9. Summary
