Self-Healing AI Pipelines: Enforcing Data Integrity through Quality-First Learning Models

In the era of big data, the reliability and effectiveness of Artificial Intelligence (AI) systems are increasingly dictated by the quality of their underlying data. Inaccurate, incomplete, or inconsistent datasets can severely compromise model accuracy, introduce bias, and reduce the interpretability and trustworthiness