Active Learning: Smart Data Labeling
Reduce labeling costs by intelligently selecting which examples to label. Active learning strategies for efficient model training.
TL;DR
Active learning selects most informative examples for labeling, reducing costs. Query uncertain examples, diverse samples, or potential errors to improve model efficiently.
Strategies
Uncertainty sampling: Label examples model is least confident about
Query-by-committee: Multiple models vote, label disagreements
Expected model change: Label examples that would change model most
Diversity sampling: Label diverse examples to cover distribution
Implementation
- Train initial model on small labeled set
- Score unlabeled data by informativeness
- Select top K examples
- Get labels (human or automated)
- Retrain model
- Repeat
Benefits
- 50-90% labeling cost reduction typical
- Faster to useful model
- Focuses human effort on hard cases
Tools
- Modal (active learning platform)
- Prodigy (annotation + active learning)
- Custom implementation
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Key Terms Used in This Guide
Training
The process of feeding data to an AI system so it learns patterns and improves its predictions over time.
Model
The trained AI system that contains all the patterns it learned from data. Think of it as the 'brain' that makes predictions or decisions.
AI (Artificial Intelligence)
Making machines perform tasks that typically require human intelligenceālike understanding language, recognizing patterns, or making decisions.
Machine Learning (ML)
A way to train computers to learn from examples and data, instead of programming every rule manually.
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