Dataset Curation Best Practices

Good datasets are the foundation of reliable ML models. This guide covers practical steps for dataset curation.

Collecting Data

• Define clear labeling guidelines and schema
• Ensure diverse and representative samples to avoid bias

Cleaning and Validation

• Remove duplicates and corrupted records
• Validate labels with multiple annotators and consensus checks

Maintenance

• Version datasets and track changes
• Monitor for data drift and retrain when distributions shift