Data Labeling Best Practices
Overview Data quality determines model quality. This guide covers labeling strategies, tools, and quality control for building reliable training datasets. Labeling Tools Tool Type Best For Label Studio Open source General purpose CVAT Open source Computer vision Prodigy Commercial NLP, active learning Scale AI Managed Large scale Amazon SageMaker GT Managed AWS integration Label Studio Setup pip install label-studio label-studio start Access at http://localhost:8080 Labeling Guidelines 1. Create Clear Instructions ## Task: Sentiment Classification Label each review as: - **Positive**: Expresses satisfaction, recommendation, or praise - **Negative**: Expresses dissatisfaction, complaints, or criticism - **Neutral**: Factual statements without emotional content ### Examples: - "Great product, highly recommend!" → Positive - "Arrived broken, waste of money" → Negative - "The package weighs 2kg" → Neutral 2. Handle Edge Cases Document ambiguous cases upfront: ...