Annotation Quality Operations: Instructions, Comments, Issues, and Review
A practical quality system for turning guidelines, annotation comments, project issues, and accepted or rejected review into continuous improvement.
Changing Ontologies Safely: Protect Existing Annotations from Schema Drift
A practical migration playbook for evolving annotation schemas without silently breaking historical labels or invalidating model comparisons.
Roles and Permissions for Annotation Teams: Governance Without Bottlenecks
A least-privilege operating model for Unitlab annotation teams that separates data access, label creation, review authority, and administration.
Build Enterprise Annotation Workflows in Unitlab
A practical guide to designing Unitlab annotation and review flows that make responsibility, correction, automation, and completion explicit.
Annotation Ontologies: Why They Matter and How to Build Them in Unitlab
A practical guide to annotation ontologies, conditional schema design, temporal properties, and the two ways to build ontology content in Unitlab.
Versioning Training Data in Unitlab: Reproducible Releases for ML Teams
A release-engineering playbook for training data: what to version, how to approve changes, and how to connect Unitlab releases to model experiments.
Human-in-the-Loop Annotation: Combining Model, Annotate, and Review Stages
How to turn model proposals, human correction, and review evidence into a controlled Unitlab workflow that improves data quality over time.
Data Annotation QA Agents in 2026: Teaching Computer Vision Datasets to Self-Correct
Annotation QA Agents: Architecture, Self-Correction Mechanisms, and Real-World Use Cases.
