Description: Quality Control Systems in the context of Edge AI are tools and methodologies designed to ensure the quality of processes and outcomes in the implementation of artificial intelligence on edge devices. These systems enable continuous monitoring and evaluation of AI models, ensuring they operate efficiently and accurately in environments where latency and connectivity may be limited. Through machine learning techniques and data analysis, quality control systems can identify anomalies, optimize performance, and improve real-time decision-making. Their relevance lies in the need to maintain high-quality standards in critical applications, such as industrial automation, healthcare, and public safety, where errors can have significant consequences. Additionally, these systems are essential for user trust, as they ensure that AI solutions implemented at the edge meet established expectations and regulations.
