AI Systems Are Software Systems
Why production AI requires the same engineering discipline as any distributed system, and how this wiki covers the full stack of AI …
Methodology
Everything as Code: Treating All Artifacts as Software
The principle of defining infrastructure, configuration, documentation, policy, video, and design as version-controlled code artifacts - and …
Version Control Fundamentals and Git
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.
CRISP-DM vs Microsoft TDSP - Data Science Project Methodologies Compared
Comparing CRISP-DM and Microsoft Team Data Science Process (TDSP) for structuring data science projects, covering phases, team roles, and …
CRISP-DM: Cross-Industry Standard Process for Data Mining
The most widely used methodology for data science and machine learning projects, providing a structured six-phase approach from business …
PRINCE2 - Projects IN Controlled Environments
A structured, process-based project management methodology originally developed by the UK government.
TDSP: Microsoft's Team Data Science Process
A structured, agile methodology for delivering data science and AI solutions in teams, emphasizing collaboration, standardized project …
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 …
Extreme Programming (XP)
Kanban for Software Development
The Agile Manifesto
The Scrum Framework
Waterfall Methodology
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 …