Having looked at resolvable problems in the financial market, the focus was then on the machine learning toolkit and finding the right tool for the problem. The favoured school of thought here was to keep this simple, opting for the simplest tool first to see if this works before moving onto a more complicated one. When it came to the more complicated tools, the most widely used were Libraries and Python however neither of these tools appeared to offer much innovation. Ultimately, the innovation was in how the tool was being used rather than what tool was being used. This led to more cutting edge discussion, offering practical studies on the use of quantum computing and AI in the energy trading market.

These models tend to be less parsimonious and have a lot of configuration overhead and sensitivity to data provenance

In another talk, Piotr Migdał gave us some tricks and tips about reproducibility and a number of best practices on team cooperation and model deployment. It's important to be aware that even a single untracked parameter tweak can lead to frustration and inefficiencies in the whole team. I can't resist to mention his good humor with the parallelism between Borja's Ecce Homo painting and model reproducibility issues.