Edge computing for machine learning. Instead of running ML models on the cloud, I train them on user's device, ask these devices to offload computation between

Edge computing for machine learning. Instead of running ML models on the cloud, I train them on user's device, ask these devices to offload computation between each other and give me the best performance out there. One good example is recommendations that work offline for you. Imagine you are listening to spotify in offline mode (with you downloaded playlist) and recommendations adapt accordingly even without internet! I built out the library for these myself, checkout https://github.com/NimbleEdge/RecoEdge https://github.com/NimbleEdge/RecoEdge