<ul><li>Cloud-based AI has transformed various industries, but it faces limitations such as latency and connectivity issues, especially in real-time applications like operating machinery and processing sensor data.</li><li>Edge AI moves decision-making closer to where data is generated, enabling real-time responsiveness, autonomy, privacy, resilience, and efficiency. It allows systems to learn and adapt locally, offering context-awareness and adaptability.</li><li>Advancements in hardware capabilities and model compression techniques have made edge AI feasible. Frameworks like TensorFlow Lite and PyTorch Mobile are making edge AI accessible to developers, leading to a strategic shift towards edge-native thinking.</li><li>While the cloud will remain important for training and coordination, real-time intelligence will increasingly reside at the edge, impacting industries like manufacturing, energy, healthcare, and more, where speed, context, and autonomy are crucial.</li></ul>

Why the Future of AI Isn’t in the Cloud — It’s at the Edge

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