,contents
Front matter
preface
acknowledgments
about this book
about the author
about the cover illustration
1 Introduction: Delivering machine learning projects is hard; let’s do it better
1.1 What is machine learning?
1.2 Why is ML important?
1.3 Other machine learning methodologies
1.4 Understanding this book
1.5 Case study: The Bike Shop
Summary
2 Pre-project: From opportunity to requirements
2.1 Pre-project backlog
2.2 Project management infrastructure
2.3 Project requirements
Funding model
Business requirements
2.4 Data
, 2.5 Security and privacy
2.6 Corporate responsibility, regulation, and ethical considerations
2.7 Development architecture and process
Development environment
Production architecture
Summary
3 Pre-project: From requirements to proposal
3.1 Build a project hypothesis
3.2 Create an estimate
Time and effort estimates
Team design for ML projects
Project risks
3.3 Pre-sales/pre-project administration
3.4 Pre-project/pre-sales checklist
3.5 The Bike Shop pre-sales
3.6 Pre-project postscript
Summary
4 Getting started
4.1 Sprint 0 backlog
4.2 Finalize team design and resourcing
4.3 A way of working
Process and structure
Heartbeat and communication plan
, Tooling
Standards and practices
Documentation
4.4 Infrastructure plan
System access
Technical infrastructure evaluation
4.5 The data story
Data collection motivation
Data collection mechanism
Lineage
Events
4.6 Privacy, security, and an ethics plan
4.7 Project roadmap
4.8 Sprint 0 checklist
4.9 Bike Shop: project setup
Summary
5 Diving into the problem
5.1 Sprint 1 backlog
5.2 Understanding the data
The data survey
Surveying numerical data
Surveying categorical data
Surveying unstructured data
Front matter
preface
acknowledgments
about this book
about the author
about the cover illustration
1 Introduction: Delivering machine learning projects is hard; let’s do it better
1.1 What is machine learning?
1.2 Why is ML important?
1.3 Other machine learning methodologies
1.4 Understanding this book
1.5 Case study: The Bike Shop
Summary
2 Pre-project: From opportunity to requirements
2.1 Pre-project backlog
2.2 Project management infrastructure
2.3 Project requirements
Funding model
Business requirements
2.4 Data
, 2.5 Security and privacy
2.6 Corporate responsibility, regulation, and ethical considerations
2.7 Development architecture and process
Development environment
Production architecture
Summary
3 Pre-project: From requirements to proposal
3.1 Build a project hypothesis
3.2 Create an estimate
Time and effort estimates
Team design for ML projects
Project risks
3.3 Pre-sales/pre-project administration
3.4 Pre-project/pre-sales checklist
3.5 The Bike Shop pre-sales
3.6 Pre-project postscript
Summary
4 Getting started
4.1 Sprint 0 backlog
4.2 Finalize team design and resourcing
4.3 A way of working
Process and structure
Heartbeat and communication plan
, Tooling
Standards and practices
Documentation
4.4 Infrastructure plan
System access
Technical infrastructure evaluation
4.5 The data story
Data collection motivation
Data collection mechanism
Lineage
Events
4.6 Privacy, security, and an ethics plan
4.7 Project roadmap
4.8 Sprint 0 checklist
4.9 Bike Shop: project setup
Summary
5 Diving into the problem
5.1 Sprint 1 backlog
5.2 Understanding the data
The data survey
Surveying numerical data
Surveying categorical data
Surveying unstructured data
