When your dataset grows past a few hundred images, manual annotation management becomes a project in itself. Who labels what? What happens after labeling? How do assets move from "unlabeled" to "reviewed" to "training-ready"? Annotation automation on Datature Nexus handles this with rule-based workflows.
What This Tutorial Covers
- Setting up automation rules that trigger on annotation events
- Auto-assigning assets to annotators based on status or tags
- Moving labeled assets to review queues automatically
- Batch operations: apply labels, change statuses, or reassign across groups
- Chaining multiple automation steps into a full labeling pipeline
Why Automation Matters at Scale
A 50-image dataset can be managed in a spreadsheet. A 50,000-image dataset cannot. Annotation automation removes the manual bookkeeping: assets flow from upload to labeling to review to training-ready without someone manually dragging them between folders. Set the rules once and they apply to every new batch of data.
Who This Is For
Team leads managing multiple annotators. ML engineers building repeatable annotation pipelines. Anyone scaling from prototype datasets to production-grade training data and finding that the labeling logistics are harder than the labeling itself.
