What is Annotation workflow?

An annotation workflow is the end-to-end process a team follows to create reliable labeled data across modalities—text, images, audio/video, LiDAR point clouds, and DICOM. It aligns people, tools, and quality checks so labels are consistent, auditable, and ready for training or evaluation.

Without a clear workflow, guidelines drift, reviewers disagree, and models learn from noisy data. A good workflow makes quality predictable: roles are defined (maker and checker/editor), edge cases are handled the same way every time, and acceptance criteria tie directly to business goals and SLAs.

Typical stages

1. Scope and schema: define the task, label taxonomy, and success criteria.
2. Guidelines and calibration: write examples (positive/negative), run small calibration rounds.
3. Pilot and refine: test on a representative sample, measure agreement, fix ambiguities.
4. Production labeling: run at scale with versioned guidelines, prelabels where useful, and workload routing by skill.
5. Review and resolution: apply maker–checker/editor steps, adjudicate disagreements, update gold tasks.
6. Acceptance and monitoring: enforce quality gates, track latency and throughput, watch for drift and retrain or re-label when data changes.

Taskmonk supports this with maker–checker/editor workflows, golden sets, agreement scoring, dashboards, and managed services—so teams can move from pilot to production without losing quality.
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