Phase 1 -
Foundations, 1 month
The basics
Real life use cases (guest speakers)
Relevant data, sufficient data, quality data
Modeling techniques: Binary classification, Regression, Multi-class classification
Concrete examples and demos
Phase 2 -
Planning, 1 month
Lessons learned from previous PdM projects
Design sprint
Understanding what kind of technical expertise is required
Initial project roadmap
Around AI; how people, processes and company culture affects implementation
Phase 3 -
Building, 2 months
Building/starting up data-infrastructure needs
Showing upside of predictive maintenance with a technical demo
Phase 4 -
Wrap up
Organization specific presentations of their progress, including demos (optional)
Sharing experiences and lessons learned
Key things to do and understand in order to continue development