Why labeling is a pipeline
Supervised learning needs labels, and producing them at scale is not a one shot task. A data labeling pipeline turns raw examples into reliable annotations through repeatable stages with quality control built in.
The stages
- Sampling, where you choose which raw items to send for labeling, often prioritizing uncertain or rare cases.
- Annotation, where humans or models assign labels following written guidelines.
- Review, where a second pass catches errors, often on a sampled subset.
- Adjudication, where disagreements between annotators are resolved into a final label.
Measuring quality
- Inter annotator agreement measures how often independent labelers pick the same answer. Low agreement signals unclear guidelines or genuinely ambiguous data.
- Gold tasks are items with a known correct answer mixed in to catch careless or low skill annotators.
Why guidelines matter
- The label definition lives in the guideline document. Ambiguous instructions produce inconsistent labels that cap model accuracy no matter how good the model is.
- Guidelines evolve, so the pipeline must track which version produced each label.
Key idea
A labeling pipeline moves raw data through sampling, annotation, review, and adjudication, using agreement and gold tasks to guarantee label quality.