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While machine learning has made its way into certain industrial applications, there are many important real-world domains, especially domains with large-scale data, that remain unexplored. There are a number of reasons for this, and they occur at all places in the technology stack. One concern is ease-of-use, so that practitioners with access to big data who are not necessarily machine learning experts are able to create models. Another is transparency. Users are more likely to want models they can easily visualize and understand. A flexible API layer is required so users can integrate models into their business process with a minimum of hassle. Finally, a robust back-end is required to parallelize machine learning algorithms and scale up or down as needed.. In this talk, we discuss our attempt at building a system that satisfies all of these requirements. We will briefly demonstrate the functionality of the system and discuss major architectural concerns and future work.
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