Description: Quality Data Management in the context of edge inference refers to a set of practices and processes designed to ensure that the data used in artificial intelligence (AI) applications on edge devices is accurate, consistent, and relevant. This is crucial as edge devices, such as sensors and cameras, collect real-time data and often operate in environments where connectivity to the cloud is limited or non-existent. The quality of data directly impacts the effectiveness of AI models, as decisions based on erroneous data can lead to ineffective or even dangerous outcomes. Key features of data quality management include data validation, data cleansing, data integration, and continuous monitoring. These practices help identify and correct errors, eliminate duplicates, and ensure that the data is representative of the environment in which it is being used. The relevance of this management lies in the growing reliance on AI in various critical applications, such as autonomous driving, healthcare, and manufacturing, where data accuracy can be decisive for the success or failure of operations. In summary, data quality management is an essential component to ensure that AI applications at the edge operate effectively and safely.
