Data Labeling
The process of annotating raw data with informative tags or labels that enable supervised machine learning models to learn from examples.
In Depth
Data labeling (also called data annotation) is the process of adding informative tags, classifications, or structured metadata to raw data, creating the labeled examples that supervised machine learning models require for training. The quality, consistency, and volume of labeled data directly determine the upper bound of model performance, making data labeling one of the most critical and resource-intensive steps in the ML development lifecycle.
Labeling tasks vary widely by data type and application. Text labeling includes sentiment classification, named entity recognition, intent detection, and text span annotation. Image labeling encompasses bounding box drawing for object detection, pixel-level masks for segmentation, keypoint annotation for pose estimation, and image classification. Audio labeling involves transcription, speaker diarization, and sound event detection. For LLM applications, labeling includes preference ranking for RLHF, instruction-response quality assessment, and factual accuracy verification.
Labeling approaches range from fully manual to heavily automated. Manual labeling by subject matter experts produces the highest quality but is slowest and most expensive. Crowdsourced labeling scales better but requires quality control mechanisms like inter-annotator agreement measurement, gold standard questions, and consensus protocols. Programmatic labeling uses heuristic rules, weak supervision, and pre-trained models to generate labels automatically, trading some accuracy for massive speed improvements. Active learning strategically selects the most informative examples for human labeling, maximizing the value of limited annotation budgets.
Modern labeling workflows increasingly leverage AI to accelerate human annotators: pre-labeling with existing models so humans only need to verify and correct, using LLMs to generate candidate labels for human review, and embedding-based similarity search to propagate labels from annotated examples to similar unannotated ones. Labeling platform providers include Label Studio (open-source), Labelbox, Scale AI, and Snorkel for programmatic labeling.
Related Terms
Training Data
The curated dataset used to train or fine-tune machine learning models, directly determining model capabilities, biases, and limitations.
Active Learning
A machine learning approach where the model strategically selects the most informative unlabeled examples for human annotation to maximize learning efficiency.
Machine Learning
A branch of artificial intelligence where systems learn patterns from data to make predictions or decisions without being explicitly programmed for each scenario.
Data Pipeline
An automated workflow that extracts, transforms, and loads data from various sources into formats suitable for AI model training and inference.
Fine-Tuning
The process of further training a pre-trained model on a domain-specific dataset to improve its performance on targeted tasks.
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Data Flywheel Operations
Standing up the flywheel: telemetry, preference signals, human feedback loops, and automated re-training that can unlock up to 98.6% inference cost reduction without losing accuracy targets.
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