Why Machine Learning at the Edge?

Machine learning algorithms, especially deep learning neural networks often produce models that improve the accuracy of prediction. But the accuracy comes at the expense of higher computation and memory consumption. A deep learning algorithm, also known as a model, consists of layers of computations where thousands of parameters are computed in each layer and passed to the next, iteratively. The higher the dimensionality of the input data (e.g., a high-resolution image), the higher the computational need. GPU farms in the cloud are often used to meet these computational requirements.