The Sequence Scope: Why Mobile Deep Learning is Tougher Than You Think

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📝 Editorial: Why Mobile Deep Learning is Tougher Than You Think

Mobile devices represent a primary runtime for our daily interactions with machine learning models. However, the vast majority of machine learning experiences in mobile devices are delivered in a server-side architecture with the machine learning model executing in a cloud environment and exposing results to mobile apps via an API. From training, personalization to computational resource consumption, the mobile deep learning paradigm presents many inefficiencies for mobile architectures. The holy grail of mobile deep learning is to build models that can execute natively and efficiently in mobile devices. These days, we have mobile deep learning frameworks in popular deep learning stacks like PyTorch or TensorFlow that easily allow you to adapt deep learning models to mobile architectures.

However, don’t get too excited yet. Mobile deep learning is one of those things that look cool and simple from the…