Managing Machine Learning Projects MEAP V04
Copyright
welcome
brief contents
Chapter 1: Introduction: Delivering Machine Learning projects is hard, letβs do it better
1.1 What is Machine Learning?
1.2 Why is ML Important?
1.3 Waterfall, Agile, Devops
1.4 Specialist Approaches for ML System Development
1.5 Understanding this Book.
1.6 Case study: The bike shop
1.7 Summary
1.8 References
Chapter 2: Pre-project: from opportunity to requirements
2.1 Pre-Project Backlog
2.2 Project Management Infrastructure
2.3 Understanding Requirements
2.3.1 Funding Model
2.3.2 Business Requirements
Business Requirements: Why?
Business Requirements: Who?
Business Requirements: What?
2.4 Data
2.5 Security & Privacy
2.6 Corporate Responsibility, Regulation & Ethical considerations
2.7 Development Architecture and Process
2.7.1 Development Environment
2.7.2 Production Architecture
2.8 Summary & Takeaways
2.9 References
Chapter 3: Pre-project: from requirements to a proposal
3.1 Project Hypothesis
3.2 Create an Estimate
3.2.1 Time and Effort estimates
3.2.2 Team Design for ML Projects
3.2.3 Project Risks
3.3 Presales/Pre-Project Administration
3.4 Pre-project/presales checklist
3.5 The Bike Shop Presales
3.6 Pre-Project Post-Script
3.7 Summary
3.8 References
Chapter 4: Sprint Zero: Getting started
4.1 Sprint Zero Backlog
4.2 Finalize Team Design & Resourcing
4.3 A Way of Working
4.3.1 Process & Structure
4.3.2 Heartbeat and Communication Plan
4.3.3 Tooling
Data Pipelining
Versioning
Data Testing
4.3.4 Standards & Practices
4.3.5 Documentation
4.4 Infrastructure Plan
4.4.1 System Access
4.4.2 Technical Infrastructure Evaluation
4.5 The Data Story
4.5.1 Data Collection Motivation
4.5.2 Collection Mechanism
4.5.3 Lineage
4.5.4 Events
4.6 Privacy and Security & Ethics Plan
4.7 Project Roadmap
4.8 Sprint 0 Checklist
4.9 Bike Shop: Project Set-up
4.10 Summary
4.11 References
Chapter 5: Sprint 1: Diving into the problem
5.1 Sprint-1 Backlog
5.2 Understanding the Data
5.2.1 The Data Survey
5.2.2 Surveying numerical data
5.2.3 Surveying categorical data
5.2.4 Surveying unstructured data
5.2.5 Reporting and using the survey
5.3 Business Problem Refinement, UX and Application Design
5.4 Building Data Pipelines
5.4.1 Data Fusion Challenges
5.4.2 Pipeline Jungles
5.4.3 Data Testing
5.5 Model Repository and Model Versioning
5.5.1 Features, Foundational Models, and Training Regimes
5.5.2 Overview of Versioning
5.6 Summary
5.7 References
Chapter 6: Sprint 1: EDA, ethics, baseline evaluation
6.1 Exploratory Data Analysis (EDA)
6.1.1 EDA Objectives
6.1.2 Summarizing and Describing Data.
6.1.3 Plots and visualisations
6.1.4 Unstructured Data
6.2 Ethics Checkpoint
6.3 Baseline Models and Performance
6.4 What if there are Problems?
6.5 Pre-modelling checklist
6.6 The Bike Shop: Pre-modelling
6.7 Summary
6.8 Works Cited