Data annotation is the process of labeling or tagging data

Starting at

$

10

About this service

Summary

Data annotation is the process of labeling or tagging data to make it understandable for machine learning models. It’s a crucial step in supervised learning, where models require labeled datasets to learn patterns and make predictions.

What's included

  • At the end of the project, the client will receive the following deliverables

    1. Annotated Datasets Structured & Labeled Data in formats such as JSON, CSV, XML, or custom formats as required. Image Annotations (bounding boxes, segmentation masks, keypoints, etc.). Text Annotations (NER, sentiment labels, intent tags, etc.). Audio Annotations (transcripts, speaker labels, emotion tags, etc.). Video Annotations (frame-by-frame labeling, object tracking, action recognition). 2. Annotation Guidelines & Documentation Detailed annotation methodology for consistency and reproducibility. Labeling schema & taxonomy used for the dataset. Edge cases and rationale behind annotation decisions. 3. Quality Assurance Reports Inter-annotator agreement scores (to measure consistency). Error analysis & corrections log. Final accuracy & validation metrics. 4. Custom Data Format & API Integration (if applicable) Conversion to client-specific data formats. Direct API integration for seamless dataset access. 5. Project Summary & Insights Key findings from the annotation process. Recommendations for improving future datasets. Suggestions for model training optimizations.


Duration

2 days

Skills and tools

Data Entry Specialist

Data Modelling Analyst

Data Analyst

Python

Python

scikit-learn

scikit-learn

Unbounce

Unbounce

Industries

Hospitality
Government & Public Administration