85 decision makers, analysts, domain experts, entrepreneurs, and academics from 13 countries came together at the Nyenrode Business University in Breukelen, Netherlands this week to attend the 1st edition of BigML’s Machine Learning Summer School in The Netherlands. These attendees represented 38 distinct organizations and 5 universities ensuring a diverse mix of expertise and experience with data-driven decision making.
The event was organized into three parts. The program for Day 1, Machine Learning for Executives, targeted business leaders as it concentrated on the economics of Machine Learning in a business setting and establishing the right strategy when adopting it. Days 2 and 3, Introduction to Machine Learning, made up the main track during which attendees got a more detailed technical look under the hood of supervised and unsupervised learning techniques supported by the BigML platform. Finally, despite the fact that most of the attendees had little or no background on Machine Learning prior to the summer school, that didn’t stop them from successfully completing a credit default risk analysis use case example as part of the workshop on Day 4, Working with the Masters.
The chairman of the event, Jan W. Veldsink of Rabobank, stressed the importance of keeping Machine Learning initiatives within business departments rather than treating them solely as IT initiatives, which makes it harder to unearth valuable, data-driven inferences. If business unit representatives aren’t included until the later stages establishing trust becomes very difficult, which causes problems when operationalizing any new insights. Ideally, each project team should contain a data domain expert, a business owner, and a Machine Learning expert.
Associate Professor Jeroen van der Velden of Nyenrode warned the audience that AI solutions also generate new types of risks (e.g., biases in training data) that must be given special attention before any adverse effects are observed in production. This is an area that doesn’t yet receive the attention it deserves from industry practitioners but as the regulatory landscape catches on it will no longer be optional.
Wibout De Klijne of Rabobank touched on the idea of combining expert opinion with Machine Learning models to arrive at the best possible outcomes within the context of fraud detection systems. Using more interpretable modeling techniques lessens any reservations or objections against black box models “taking over”.
Enrique Dans, of IE University provided a deep dive into the business opportunities possible thanks to Machine Learning such as personalized healthcare, but also mentioned that it likely will be a bumpy ride as experts (e.g., doctors) incorporate models into their everyday workflows.
BigML’s Chief Scientist Professor Tom Dietterich gave the audience a quick tour of the latest and greatest from the world of Machine Learning research getting them to go a level deeper on topics like contextual bandits, deep learning interpretability and continuous learning agents.
BigML client Juriblox , as well as partners T2Client and A1 Digital, presented reference Machine Learning use cases such as predicting logistics expedition outcomes, energy trading, and others in the automotive and legal verticals.
Good news for those that could not make it to this edition, but the presentions from the summer school can be found here. However, we’re looking forward to hosting you at the next Machine Learning summer school or training event!