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Deploying Large Machine Learning Models on Small Devices
The chapter explores the challenges of deploying large machine learning models on small devices, emphasizing the importance of real-time applications and data privacy. It discusses model compression techniques, focusing on the development of the on-device WISPR kit for translation transcription applications. The conversation highlights optimizations for performance and compression, balancing generic tools and bespoke techniques for efficient deployment.