Crowdsourced Labeling

Crowdsourced labeling is a data annotation approach where large datasets are labeled by distributing tasks to a distributed workforce, often through online platforms. It leverages human intelligence to classify, tag, or annotate data such as images, text, or audio, which is essential for training machine learning models. This method enables rapid scaling of labeling efforts at a lower cost compared to in-house teams.

Also known as: Crowdsourced Annotation, Human-in-the-Loop Labeling, Crowd Annotation, Data Labeling via Crowdsourcing, Crowdsourced Data Tagging

Why learn Crowdsourced Labeling?

Developers should use crowdsourced labeling when building machine learning models that require large volumes of labeled data, such as for computer vision, natural language processing, or audio recognition projects. It is particularly valuable in scenarios where data annotation is time-consuming or resource-intensive, allowing teams to accelerate model development and improve accuracy by accessing diverse human perspectives.

Learning Resources

• docs: Crowdsourced Labeling Guide by Amazon Mechanical Turk
• course: Crowdsourcing for Machine Learning on Coursera
• video: Introduction to Data Labeling with Crowdsourcing
• book: Human-in-the-Loop Machine Learning Book