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In the era of the experience economy, every customer wants tailor-made products that fit their needs. eCommerce and online retail platforms are now using Al-based solutions to intrigue customers. AI helps these companies deliver refined search results and suggest relevant products to online shoppers based on their preferences. Data annotation is imperative to all Al/ML-based operations. The data annotation tools market is expected to reach over 10 billion USD by 2028.  It refers to the categorization and labeling of data based on its specific use. High-quality and accurate data annotation requires human effort where users label and classify information.  Based on this data, AI/ML models then apprehend essential attributes from the database. With the help of data annotation, Al also analyzes different product attributes and recommends related products in online and offline shops. Data Annotation - Use Cases And Benefits To The Retail And eCommerce Industries Following are the ways in wh

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