How to effectively manage AI/ML projects
summary
Project management approaches tailored to AI matter: use Data Driven Scrum (DDS) with sprint decisions driven by data not fixed calendars, keep an updated risk assessment and contingency plan, and assign clear owners for quality and delivery. Example: a seven working day sprint on thermal imagery collects approximately 000 labeled images, measures precision/recall, finds YOLOv5 outperformed YOLOX on chosen metrics, then re-plans the next sprint. Address EU AI Act and privacy by anonymizing gender/age/emotion to stay out of biometric scope.
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chapters (11)
- 0:00Introduction to AI project management
- 1:34Early project risks and planning
- 3:10Risk assessment and contingency plans
- 5:47Project manager's dual role
- 7:23Why Agile is essential for AI projects
- 9:45Data-driven Scrum explained
- 11:38Real-world sprint example
- 15:06AI regulations and privacy concerns
- 18:02Compliance and data handling
- 20:58Global privacy regulations overview
- 21:19Q&A session
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