Before the Model: Mapping the Data Minefield in Edge AI

This episode outlines significant data challenges inherent in Edge AI deployments, moving beyond controlled lab settings into real-world applications. It highlights issues such as collecting representative data that accurately reflects diverse operational conditions and managing sensor variability across different devices.

It also addresses the high cost and time associated with data labeling, particularly for specialized tasks, and the problem of class imbalance where critical events are rare. Furthermore, it details how data drift can degrade model performance over time, the scarcity of relevant public datasets for niche edge cases, and the non-trivial nature of data preprocessing.

Finally, the podcast discusses challenges posed by noisy or low-quality data, the complexity of data validation, limited dataset sizes common in edge scenarios, and constraints related to on-device storage.