001. Chapter 1. Introduction Delivering machine learning projects is hard; lets do it better
002. Chapter 1. Why is ML important
003. Chapter 1. Other machine learning methodologies
004. Chapter 1. Understanding this book
005. Chapter 1. Case study The Bike Shop
006. Chapter 1. Summary
007. Chapter 2. Pre-project From opportunity to requirements
008. Chapter 2. Project management infrastructure
009. Chapter 2. Project requirements
010. Chapter 2. Data
011. Chapter 2. Security and privacy
012. Chapter 2. Corporate responsibility, regulation, and ethical considerations
013. Chapter 2. Development architecture and process
014. Chapter 2. Summary
015. Chapter 3. Pre-project From requirements to proposal
016. Chapter 3. Create an estimate
017. Chapter 3. Pre-salespre-project administration
018. Chapter 3. Pre-projectpre-sales checklist
019. Chapter 3. The Bike Shop pre-sales
020. Chapter 3. Pre-project postscript
021. Chapter 3. Summary
022. Chapter 4. Getting started
023. Chapter 4. Finalize team design and resourcing
025. Chapter 4. Infrastructure plan
026. Chapter 4. The data story
027. Chapter 4. Privacy, security, and an ethics plan
028. Chapter 4. Project roadmap
029. Chapter 4. Sprint 0 checklist
030. Chapter 4. Bike Shop project setup
031. Chapter 4. Summary
032. Chapter 5. Diving into the problem
033. Chapter 5. Understanding the data
034. Chapter 5. Business problem refinement, UX, and application design
035. Chapter 5. Building data pipelines
036. Chapter 5. Model repository and model versioning
037. Chapter 5. Summary
038. Chapter 6. EDA, ethics, and baseline evaluations
039. Chapter 6. Ethics checkpoint
040. Chapter 6. Baseline models and performance
041. Chapter 6. What if there are problems
042. Chapter 6. Pre-modeling checklist
043. Chapter 6. The Bike Shop Pre-modelling
045. Chapter 7. Making useful models with ML
046. Chapter 7. Feature engineering and data augmentation
048. Chapter 7. Making models with ML
049. Chapter 7. Stinky, dirty, no good, smelly models
050. Chapter 7. Summary
051. Chapter 8. Testing and selection
052. Chapter 8. Testing processes
053. Chapter 8. Model selection
054. Chapter 8. Post modelling checklist
055. Chapter 8. The Bike Shop sprint 2
056. Chapter 8. Summary
057. Chapter 9. Sprint 3 system building and production
058. Chapter 9. Types of ML implementations
059. Chapter 9. Nonfunctional review
060. Chapter 9. Implementing the production system
061. Chapter 9. Logging, monitoring, management, feedback, and documentation
062. Chapter 9. Pre-release testing
063. Chapter 9. Ethics review
064. Chapter 9. Promotion to production
065. Chapter 9. You arent done yet
066. Chapter 9. The Bike Shop sprint 3
067. Chapter 9. Summary
068. Chapter 10. Post project
069. Chapter 10. Off your hands and into production
070. Chapter 10. Team post-project review
071. Chapter 10. Improving practice
072. Chapter 10. New technology adoption
073. Chapter 10. Case study
074. Chapter 10. Goodbye and good luck
075. Chapter 10. Summary
