Agile AI Delivery - Iterative Development for AI Projects
Adapting Agile methodologies for AI project delivery: sprint structures, uncertainty management, and balancing exploration with production …
Agile
Agile for AI Projects - Adapting Agile to Machine Learning
How to apply Agile principles to AI and ML projects, addressing the unique challenges of experimentation, data dependencies, and uncertain …
Agile vs Waterfall for AI Projects - A Structured Comparison
A side-by-side comparison of Agile and Waterfall methodologies for AI projects, with decision criteria and hybrid approach recommendations.
AI-Facilitated Sprint Retrospective Analysis
AI analyzes sprint metrics, commit history, and team feedback to generate retrospective insights and identify recurring patterns across …
Backlog Prioritization for AI Projects
Frameworks and techniques for prioritizing AI project backlogs, balancing business value, technical risk, data readiness, and research …
Scrum for Machine Learning Teams - A Practical Guide
How to implement Scrum in ML teams, covering sprint cadence, role adaptations, backlog structure, and ceremony modifications for data …
Scrum vs Kanban for Machine Learning Teams
Comparing Scrum and Kanban frameworks for ML teams, covering ceremonies, metrics, work management, and guidance on which fits different ML …
Sprint Planning for AI Projects - Getting It Right
How to run effective sprint planning sessions for AI and ML teams, covering estimation techniques, capacity planning, and handling research …
Waterfall vs Agile for AI Projects - When Each Approach Works
A practical comparison of waterfall and agile methodologies for AI and ML projects, including hybrid approaches and decision criteria for …
YAGNI Principle - You Aren't Gonna Need It
A software development principle from Extreme Programming stating that functionality should not be added until it is actually needed.
Extreme Programming (XP)
Kanban for Software Development
The Agile Manifesto
The Scrum Framework
Open Practice Library
What the Open Practice Library is, its key practices for AI projects, and how it structures discovery and delivery for teams building …
Open Practice Library for AI Projects - Discovery to Delivery
Applying Open Practice Library practices to AI: Event Storming for AI use case discovery, Impact Mapping for AI value, User Story Mapping …
From AI Idea to Working Prototype in 3 Workshops
A structured three-workshop methodology that takes an organization from AI curiosity to a validated, buildable prototype with stakeholder …
WSJF - Weighted Shortest Job First
Definition, formula, and how to adapt WSJF for scoring and prioritizing AI use cases and backlog items.