Multisource weak supervision turns unlabeled examples into a noisy training dataset by combining imperfect, inexpensive labeling functions such as accumulated rule-based overrides. section 13.4
Use it when a long retraining or release cycle makes immediate model improvement impractical:
- Track overrides instead of letting forgotten rules become technical debt. section 13.4
- Treat collections of overrides as labeling sources for generating noisy examples. section 13.4
- Use the resulting dataset as a foundation for later model training. section 13.4
Production overrides as training signals
A team can correct recurring model failures with inexpensive rules, record those corrections, and later use the accumulated signals to improve the model rather than repeatedly handling each case manually. section 13.4
