Data Labeling
The process of annotating data with labels or tags that machine learning models can learn from.
In-Depth Explanation
Data labeling (annotation) is the process of adding informative tags to raw data, creating labeled datasets for supervised machine learning. It's often the most time-consuming part of ML projects.
Labeling types:
- Classification: Assign categories
- Bounding boxes: Object detection
- Segmentation: Pixel-level labels
- Named entities: Text annotations
- Sentiment: Positive/negative/neutral
- Transcription: Speech to text
Labeling approaches:
- Manual labeling
- Crowdsourcing
- Active learning (prioritise uncertain samples)
- Weak supervision (programmatic labels)
- Self-supervision (automatic labels)
Quality considerations:
- Inter-annotator agreement
- Clear labeling guidelines
- Quality control processes
- Edge case handling
Business Context
Labeled data is the fuel for supervised learning. Quality labels are more important than quantity - garbage labels mean garbage models.
How Clever Ops Uses This
We design labeling pipelines for Australian businesses, balancing quality, cost, and speed to create effective training datasets.
Example Use Case
"Labeling customer support tickets with intent categories and sentiment, creating training data for an automated routing and prioritisation system."