6 Tips to Avoid Pitfalls in Data Annotation
summary
The talk examines common pitfalls in data annotation for machine learning projects and gives six practical recommendations to avoid them. It covers issues like project complexity, resource constraints, and data security risks, and provides a checklist for designing an efficient annotation strategy whether using in-house teams or outsourcing.
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