Quality unboxed: 3 steps towards data labeling excellence
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50% of the time of labeling projects is around data quality.
The increasing focus on the scale, speed, and cost of building and improving datasets has impacted the quality of the data and thus the quality of the models.
While the quality of data sets remains everyone's primary concern, the way it is measured and managed in practice is poorly understood – and sometimes just plain wrong.
During this 45-minute webinar replay, you will learn about:
- The impact of labeled datasets issues on your models;
- The main root causes of quality issues in labeled datasets;
- The 3 lines of defense on data quality in annotation project.