Inference of convolutional neural network models is algorithmically straightforward, but to get the fastest performance for your application there are a few pitfalls to keep in mind when deploying. A number of factors make efficient inference difficult, which we will first step through before diving into specific solutions to address and resolve each. By the end of this article, you will be armed with four tools to use before building your system. Broadly speaking, convolutions are all about sliding a function over something else. In the context of image data, we slide a window over pixels with three channels (RGB) and apply the same function on each window.