Predictive maintenance requires edge computing to process sensor data locally, rather than in the cloud, to achieve the necessary real-time response for detecting potential equipment failures or hazardous conditions. This approach minimizes latency and reduces data transmission costs, which is critical for industrial and safety applications. Developers and engineers must optimize AI models for resource-constrained edge devices and manage the trade-offs between performance and computational power.
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