EnFuse Solutions Blogs

In traditional software development, the efficiency of the delivered product depends on its code quality. The same principle applies to Artificial Intelligence (AI) and Machine Learning (ML) projects. The quality of the data model output is dependent on the quality of its data labels. Poorly labeled data leads to poor quality of data models. Why does this matter so much? Low-quality AI and ML models can lead to: An adverse impact on SEO and organic traffic (for product websites) An increase in customer churn Unethical errors or misrepresentations As data annotation (or labeling) is a continuous process, AI and ML models need continuous training to achieve accurate results. This requires data-driven organizations to avoid committing crucial mistakes in the annotation process. Here are six of the most common mistakes to avoid in data annotation projects: 1. Assuming the Labeling Schema Will Not Change A common mistake among data annotators is to design the labeling schema (in new proje...