Automating Machine Learning with Images

Following our previous posts on Image processing in BigML, the turn has arrived to discuss automation for datasets with images. As BigMLers will already know, BigML offers automation that can be used on the server-side thanks to the WhizzML language, which has been designed especially for Machine Learning tasks, but it also offers client-side bindings for many programming languages. In this post, we'll review the Python bindings approach. From the Machine Learning point of view, each image is a source for many fields, like the light levels in some regions, their color information, or the shapes it contains. In that sense, we need to think of an image as a composed field, just as we could think about other composed fields like text or date-time fields.