1) Data import - connects to data sources like twitter, salesforces, web, database etc
2) Data filter - automatic data filtering and normalization, user selects the interesting target to predict.
3) Data analytics - the systems suggests automatically which ML methods to use. The user just clicks then ones that fit. (Very high level - like classify, regress etc.).
Once a topic is selected, few algos are run in parallel and the results shown to the user.
4) Publish - once the user is happy with the results, he can publish in one of several visual forms like graphs, geographical maps etc. The publish creates either an interactive web page or pdf with the results.
The results are very impressive. around 70 programmers had the ML training. In 4 months they have created around 30 projects which many of them are pushed towards deployment in production.
One case study he gave is customer leads prediction. You simply select data source = salesforce, you select the target (sell/ no sell), the ML method (classify), after a few minutes you an interactive application with zoomable US maps that shows you sales predictions. Everything is highly visual and appealing.