How to Label Data for Machine Learning: A Step-by-Step Workflow

Labeling data for machine learning is a complex, ongoing process that requires a structured workflow to ensure consistency and accuracy, which directly impact model performance. Rather than being a one-time task, data labeling involves defining an ontology, curating datasets, deciding on a labeling model, utilizing AI-assisted pre-labeling, and establishing quality assurance (QA) and inter-annotator agreement processes. It is crucial to version labels and use active learning to continuously improve model performance. Teams often encounter issues such as ontology drift, annotator inconsistency, and inadequate QA capacity as they scale, highlighting the importance of treating data labeling as an engineering problem with defined stages and checkpoints. Choosing a suitable platform that supports the entire labeling workflow, rather than just individual tasks, can help avoid common pitfalls and enhance the efficiency and quality of the labeling process.

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