How to scale AI with a high degree of customization

In a previous post, I outlined four challenges to scaling AI: customization, data, talent, and trust. In this post, I'm going to dig deeper into that first challenge of customization. Scaling machine learning programs is very different to scaling traditional software because they have to be adapted to fit any new problem you approach. As the data you're using changes (whether because you're attacking a new problem or simply because time has passed), you will likely need to build and train new models. This takes human input and supervision. The degree of supervision varies, and that is critical to understanding the scalability challenge.