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.
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.
Building Multimodal Datasets for AI: Image, Video, Audio, Text, and Documents
A practical architecture for assembling multimodal training data in Unitlab without losing identity, alignment, schema clarity, or release lineage.
Datasets vs. Releases in Unitlab: What to Use, When, and Why
A practical decision guide to Unitlab datasets and releases: working collections for active operations versus versioned handoffs for downstream use.
Data Curation in Unitlab: Assets, Filters, Folders, and Embedding Views
A hands-on data-curation playbook for turning raw assets into representative, reviewable, and reproducible working datasets in Unitlab.
Documents and Multimodal Releases: Building Rich Training Data in Unitlab
A live-product look at document data in Unitlab, multimodal release anatomy, verified capabilities, and a reproducible handoff pattern for ML teams.
Medical Data Annotation in Unitlab: Structured Labels for Clinical AI
How Unitlab combines synchronized DICOM and 3D viewing with structured clinical properties—and how teams can validate a responsible medical labeling pilot.