Machine learning is growing in importance in industry, sciences, and many other fields. In many and perhaps most of these applications, users need to trade off competing goals. Machine learning, however, has evolved around the optimization of a single, usually narrowly-defined criterion. In most cases, an expert makes (or should be making) trade-offs between these criteria which requires high-level (human) intelligence. With interactive customization and optimization the expert can incorporate secondary criteria into the model-generation process in an interactive way. In this paper we develop the techniques to perform customized and interactive model optimization, and demonstrate the approach on several examples. The keys to our approach are (i) a machine learning architecture which is modular and supports primary and secondary loss functions, while users can directly manipulate its parameters during training (ii) high-performance training so that non-trivial models can be trained in real-time (using roofline design and GPU hardware), and (iii) highly-interactive visualization tools that support dynamic creation of visualizations and controls to match various optimization criteria.
