We continue to be excited by the TinyML technique and the ability to create machine learning (ML) models designed to run on low-powered and mobile devices. Until recently, executing an ML model was seen as computationally expensive and, in some cases, required special-purpose hardware. While creating the models still broadly sits within this classification, they can now be created in a way that allows them to be run on small, low-cost and low-power consumption devices. If you've been considering using ML but thought it unrealistic because of compute or network constraints, then this technique is worth assessing.
Until recently, executing a machine-learning (ML) model was seen as computationally expensive and in some cases required special-purpose hardware. While creating the models still broadly sits within this classification, they can be created in a way that allows them to be run on small, low-cost and low-power consumption devices. This technique, called TinyML, has opened up the possibility of running ML models in situations many might assume infeasible. For example, on battery-powered devices, or in disconnected environments with limited or patchy connectivity, the model can be run locally without prohibitive cost. If you've been considering using ML but thought it unrealistic because of compute or network constraints, then this technique is worth assessing.
