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.
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.
How to Connect Cloud Storage to Unitlab
See the actual Unitlab connection screens, the credentials each provider needs, and a safe way to validate a cloud-backed annotation source.
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.
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.
Bring Your Own AI Model to Unitlab: A Practical Enterprise Integration Guide
A live-product guide to registering an external annotation model in Unitlab and designing the security, mapping, validation, and rollback controls around it.
Find Similar in Unitlab: Turn One Trusted Annotation into Many Candidates
A practical guide to seed-object quality, confidence thresholds, candidate review, and measuring Unitlab Find Similar on repeated visual patterns.
Prompt-Based Auto-Labeling: Detect Every Object in a Class with Unitlab
How class-aware prompting can accelerate object detection while keeping scope, review, and acceptance under human control.