Data Labeling
Definition
The process of tagging data with labels or annotations to teach AI models what patterns to recognize, crucial for supervised learning success.
Use Cases
- Waymo: Labeling camera and LiDAR data to train perception models for autonomous driving (e.g., vehicles, pedestrians, cyclists, lane boundaries). — Uses large-scale annotation pipelines combining human labelers with tooling and quality checks to produce high-quality labeled sensor datasets for model training and evaluation. (Improved perception model accuracy and reliability, supporting safer autonomous driving behavior and continuous model iteration.)
- Pinterest: Labeling and curating content signals to improve visual search and content understanding (e.g., identifying objects or themes in images). — Combines human-in-the-loop labeling with ML-assisted workflows to create training data for computer vision models used in search and recommendation features. (Better relevance in visual discovery experiences and improved user engagement through more accurate content understanding.)
- Amazon: Labeling product and logistics-related images/text to support automation (e.g., package handling, product categorization, and quality checks). — Uses human annotation and quality control processes, often augmented by ML-assisted labeling, to generate training datasets for internal computer vision and NLP models. (Higher automation accuracy and operational efficiency by reducing model errors and improving consistency across large-scale workflows.)
Provider Equivalents
- AWS: Amazon SageMaker Ground Truth
- Azure: Azure Machine Learning data labeling
- GCP: Vertex AI Data Labeling
- OCI: OCI Data Labeling
Frequently Asked Questions
- What's the difference between data labeling and data annotation?
- They’re often used interchangeably. In practice, “labeling” usually means assigning a category (like “cat” vs “dog”), while “annotation” can be broader and include detailed markings like bounding boxes, polygons, keypoints, or text highlights.
- When should I use data labeling?
- Use data labeling when you’re training or evaluating supervised ML models and you don’t already have reliable ground-truth labels. It’s especially important for computer vision (object detection/segmentation), NLP (intent/entity extraction), and any use case where model quality depends on accurate examples.
- How much does data labeling cost?
- Cost depends on (1) volume of items to label, (2) label complexity (classification vs bounding boxes vs segmentation), (3) required accuracy and review steps, (4) labeler type (in-house experts vs vendor workforce), and (5) tooling/platform fees. Complex tasks like pixel-level segmentation typically cost more per item than simple classification, and adding multi-pass review increases cost but improves quality.
Category: ai-ml
Difficulty: intermediate
Related Terms
See Also