👉 The processed project is a refined and optimized version of the initial data or model, designed to enhance performance, reduce size, and improve efficiency. It involves techniques such as data cleaning, normalization, feature engineering, model pruning, and sometimes even using advanced algorithms or architectures to achieve better accuracy and faster inference times. This processed project is typically more compact and robust, making it suitable for deployment in resource-constrained environments while maintaining or even improving its predictive capabilities.
