Labelbox

Labelbox holds a position in the machine learning pipeline that often gets less attention than model training but is no less important: data annotation. Before a model can learn to detect objects, classify images, transcribe audio, or understand natural language, someone — or increasingly something — must label the training examples. Labelbox provides the platform where human annotators, ML-assisted pre-labelling systems, and quality review workflows converge. Large enterprises use it for mission-critical labelling pipelines that feed production models; research teams use it for smaller-scale annotation projects where accuracy is paramount.

The platform's architecture separates the annotation editor — the browser-based interface where labellers work through assets — from the data management layer that stores assets, label schemas, and completed annotations. Large datasets, particularly those containing high-resolution images or video, require efficient asset delivery to annotators' browsers, which puts CDN and media delivery infrastructure under load during active labelling sessions. The Label Assist feature, which pre-populates labels using a model prediction so human annotators only need to correct rather than create from scratch, adds an ML inference dependency to the annotation workflow.

During Labelbox incidents, labellers typically encounter the annotation editor loading but failing to display the current asset — the image, video frame, or document that needs labelling — because the asset delivery layer is degraded. Labels submitted through the editor may not save to the backend, appearing to accept the submission but reverting on page reload. Workflow progression — where completing one task automatically loads the next — can stall when the task queue cannot be fetched. Data exports, which ML engineers depend on to retrieve completed annotations for training runs, may time out for large datasets when the export job queue is backed up. The API, used to programmatically manage datasets and pull annotation results, returns 500 errors during backend degradation.

Outage.gg monitors Labelbox platform status using community reports from ML teams and annotation operators. If the editor is failing to load assets, label submissions are not saving, or exports are timing out, the live status page shows current incident data.