Unitlab AI: Advanced Medical Data Annotation for Clinical Imaging
A post on how Unitlab AI solves many existing medical annotation problems and improves existing workflows.
Meta Enters the Coding-Agent Race With Muse Code
Meta has paired Muse Spark 1.2 with Muse Code, its first public coding agent. Here is how it compares with Codex, Claude Code, and Gemini—and why multimodal data and expert review will shape the next frontier.
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.
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.