Data labeling and annotation is the process of attaching ground-truth labels to raw data — text, images, audio, or video — so that supervised machine learning models can learn from clear examples.
It covers annotation strategies, measuring agreement between annotators, choosing labeling tools, and balancing the cost, speed, and quality tradeoffs engineers face on real projects. Also known as: Data Annotation, Data Labeling
What this topic covers
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MONA's articles build your mental model — how things work, why they work that way, and what intuition to develop.
Concepts covered
Inter-annotator agreement measures label quality beyond chance. Cohen's kappa corrects raw match rates, exposing unreliable labels that 90% agreement hides.
Label noise averages an estimated 3.4% across major ML test sets, distorting supervised model accuracy and even flipping benchmark leaderboard rankings.
Data labeling assigns ground-truth labels to raw data so supervised models learn a mapping. Label noise propagates into model errors geometrically.
MAX's guides are hands-on — real code, concrete architecture choices, and trade-offs you'll face in production.
Tools & techniques
A data labeling pipeline pairs an annotation tool — Label Studio, Labelbox, or CVAT — with active learning to label the uncertain samples first.
DAN tracks how this domain is evolving — which models, techniques, and benchmarks are reshaping 2026.
Models & benchmarks
Updated June 2026
Meta's ~$14.3B stake in Scale AI split the data-labeling market: rivals like Surge, Mercor, and Snorkel absorbed the customer exodus heading into 2026.
ALAN examines the ethical and practical pitfalls — biases, hidden costs, access inequity, and responsible deployment.
Risks & metrics
Data labeling depends on workers earning roughly $1.32–$2/hour to filter traumatic content, while annotator bias quietly shapes what AI treats as truth.