Data Labelling & Annotation Service — Up to 1,000 Records

I will label and annotate up to 1,000 data records for your machine learning training dataset — covering text classification labels, sentiment tags, named entity annotations, image bounding boxes, image classification labels, or custom categorical labels defined by your annotation schema. Labelled training data is the foundation of supervised machine learning; the quality and consistency of annotations directly determines the quality of every model trained on them, making professional annotation far more valuable than crowdsourced or rushed self-annotation.

The annotation process covers annotation schema review and clarification before starting, consistent label application following your guidelines, inter-annotator agreement checking on a sample set to validate consistency, ambiguous case documentation with resolution decisions recorded, and delivery of annotated data in your required format (CSV with label columns, JSON with annotation objects, or YOLO/COCO format for image tasks). An annotation quality report is included covering label distribution and flagged edge cases.

This service suits ML teams, data scientists, and AI developers building supervised learning datasets for text, image, or structured data tasks who need reliable, consistent annotations they can trust to train production-quality models