How do we scale high-quality labeling?
Labeling is the unsung hero of ML success.
How It Works:
Combine active learning to select informative samples with managed labeling platforms and QA workflows that include consensus and expert review.
Key Benefits:
- Cost control: Label only what?s needed.
- High accuracy: Consensus boosts correctness.
- Scalability: Label thousands of samples efficiently.
Real-World Use Cases:
- Autonomous vehicles: Label road signs and obstacles.
- Healthcare NLP: Annotate medical entities.
FAQs
What is active learning?
How track labeler performance?
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