Active Learning Pipeline

Create an active learning system to optimize data labeling.

Setup:
- Task type: [CLASSIFICATION/DETECTION/NER/etc.]
- Initial labeled data: [SIZE]
- Unlabeled pool: [SIZE]
- Labeling budget: [SAMPLES PER ROUND]

Active learning components:
1. Query strategies:
   - Uncertainty sampling
   - Query by committee
   - Expected model change
   - Diversity sampling
2. Model training:
   - Initial model training
   - Incremental updates
   - Ensemble for uncertainty
3. Selection pipeline:
   - Batch selection
   - Diversity-uncertainty tradeoff
   - Cold start handling
4. Human-in-the-loop:
   - Labeling interface
   - Quality control
   - Annotation guidelines
5. Stopping criteria:
   - Performance plateau
   - Budget exhaustion
   - Confidence threshold
6. Evaluation:
   - Learning curves
   - Sample efficiency
   - Comparison with random
7. Production:
   - Pipeline orchestration
   - Progress tracking
   - Model versioning

Integrate with annotation tools (Label Studio, etc.).