How to Automate the Assessment of Training Data Quality

The quantity problem in machine learning refers to the need for large amounts of labeled training data to train models effectively, while the quality problem involves ensuring that the labels are accurate and reliable. Poor data quality can lead to model errors and decreased performance, particularly in applications such as autonomous vehicles and medical diagnosis where accuracy is crucial. To address this issue, Encord has developed a fully automated label and data quality assessment tool that uses semi-supervised learning algorithms to detect likely errors within projects and provides an automated ranking of labels by probability of error. This tool can help machine learning teams improve the quality of their training data more efficiently than manual review processes, which are time-consuming and unscalable.

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