This episode of The Data Science Podcast dives into TinyML—the practice of running machine learning models on microcontrollers and edge devices with limited power and memory. Lucas and Luna explore a specific case: a smart agriculture startup that used a TensorFlow Lite model on an Arduino board to detect crop disease in real time, processing images locally without sending data to the cloud. They discuss the trade-offs: model compression techniques like pruning and quantization, the challenge of balancing accuracy against a 256KB memory budget, and why TinyML is gaining traction in industries from manufacturing to healthcare. The hosts also touch on the broader movement toward on-device AI, privacy benefits, and the emerging toolchain from Google's TensorFlow Lite Micro to ARM's Ethos-U55. If you've ever wondered how data scientists shrink neural networks to run on a battery-powered sensor, this episode is for you.

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