DDD for Machine Learning

This is a presentation from my colleague Dr. Larysa Visengeriyeva. This slide deck was the base for our workshop at DDD Europe

Transcript

1. Dr. Larysa Visengeriyeva Machine Learning Use Cases with DDD and ML Design Canvas
2. What kind of problems?
3. What kind of problems?
4. ML Use Cases Dimensions
5. When?
6. How?
7. Knowledge Crunching Event Storming For Finding AI Opportunities
8. Event Storming + ML Canvas
10. Finding Pain Points: Candidates for ML-Use Cases? ( 0DLOb HQWKLHOWb NHLQHJ¾OWLJHb 'DWHL 0Z6Wb HUNDQQW *LQL %LQ¦UGDWHLHQb 3')MSJb SQJ b H[SRUWLHUW 0DLOJXQ .DWHJRULHb HLQWUDJHQ (0DLOPLWb %HOHJDQb *RULOODb JHVFKLFNW 2&5b (UNHQQXQJ I¾Ub %LQ¦UGDWHL 0HWD'DWHQb -621b JHOLHIHUW (QWK¦OW)HOGb :HUWb .RPEL QDWLRQHQ %HOHJb HUVWHOOWDXVb *LQL-621 %HOHJb %HQXW]HU LQb ]XJHZLHVHQ %HOHJΖPSRUWSHU0DLO %HOHJΖPSRUWSHU8SORDG .DWHJRULHb PXVVDXWRPDW HUNDQQWb ZHUGHQ ΖFKZLOOPLWb GHP6\VWHPb QLFKWb LQWHUDJLHUHQ )XQNWLRQLHUWb QLFKWEHLb DOOHQb .DWHJRULHQ ΖFKZLOOPLWb GHP6\VWHPb QLFKWb LQWHUDJLHUHQ
11. Using Domains For Prioritization
12. How to Structure ML Projects
13. Structuring ML Projects
14. Structuring ML Projects
15. Value Propositions
16. ML Task (Start with a heuristic)
17. Data Sources
18. Features
19. Offline Evaluation
20. Decisions
21. Making Predictions
22. Collecting Data
23. Building Models
24. Live Evaluation and Monitoring