Large-Scale Data Labelling & Annotation Project Management

I will manage a large-scale data labelling and annotation project for up to 10,000 records — covering annotation schema design, annotator guideline documentation, labelling tool setup (Label Studio, CVAT, or Prodigy), quality assurance protocol with inter-annotator agreement measurement, disagreement resolution, and delivery of a production-quality annotated dataset with a full quality report. Large annotation projects fail most commonly not because of labelling tool limitations but because of inadequate schema design, poor annotator guidelines, and absent quality control — producing datasets that look complete but contain systematic label inconsistencies that corrupt every model trained on them.

The project management scope covers annotation schema design with edge case guidance, labelling tool configuration, annotator onboarding material, batch QA reviews with feedback loops, inter-annotator agreement tracking (Cohen's Kappa or Krippendorff's Alpha), systematic bias identification and correction, final dataset export in your required format (CSV, JSON, YOLO, COCO, or spaCy), and a project report covering annotation statistics, quality metrics, and unresolvable edge cases documented for your ML team's reference.

This service suits ML teams and AI companies running production annotation projects at scale who need professional project management and quality assurance rather than simply more annotators.