Data Labeling
Last updated 2026.02.13Definition
Data Labeling is the process of adding informative tags or annotations to unlabeled raw data, making it suitable for AI model training. By marking ground truth information on images, text, sensor data, etc., it builds training datasets for supervised learning.
Applications in Manufacturing
Defect Inspection AI Development
The most common use case in manufacturing is building vision inspection systems. Product images are labeled with tags like 'good', 'defective', 'scratch', or 'discoloration' to train defect detection models.
Predictive Maintenance Model Creation
Equipment sensor data is labeled with states such as 'normal', 'anomaly signs', or 'failure' to develop AI that predicts equipment conditions. Historical failure records are connected with sensor patterns to generate training data.
Process Optimization
Production process parameter data is labeled with tags like 'optimal condition' or 'needs improvement' to train process control AI.
Key Points
- Quality determines model performance: Inaccurate labeling directly causes AI malfunctions
- Domain expertise required: Knowledge from experienced shop floor workers determines labeling accuracy
- Continuous improvement: Model updates through re-labeling when new defect types are discovered
- Cost-effectiveness: Consider long-term AI performance gains versus initial investment