I think this is a fantastic read, thank you, Vincent, I'm using it in our experiments as we speak. At the same time, for publishing we do need to learn something general, beyond just the way you solved your specific problem. A set of simulators would be great to demonstrate and to generalize not only the particular methods you tried but also the experimental protocol itself. I know this is tough, a good simulator should reproduce not necessarily the same scores but the same rank ordering of the methods as your results on the real system. Then, playing with the simulators and observing the changing rankings can help us to understand what makes methods better or worse on certain types of systems.

Start with y. Concentrate on formalizing the predictive problem, building the workflow, and turning it into production rather than optimizing your predictive model. Once the former is done, the latter is easy.

If you like the blog, check out this podcast on the same topic.

There is no debate on how a well-functioning predictive workflow works when it is finally put into production. Data sources are transformed into a set of features or indicators X, describing each instance (client, piece of equipment…

Authors’ comments (Akin Kazakçi, Mehdi Cherti, Balázs Kégl)

A sample of new types of “hand-written” symbols discovered by the model.

Computational creativity has been gaining increased attention in the machine learning community and in the larger public. The wave started by the freaky psychedelic images of deep dream, generated by saturating neurons starting from a natural image (an artificial “migraine”?). Style transfer

Balázs Kégl

Head of AI research, Huawei France, previously head of the Paris-Saclay Center for Data Science, co-creator of RAMP (http://www.ramp.studio).

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