2026-03-11 –, Main Hall
Machine learning now plays a very important role in a wide range of scientific fields. However many researchers in these scientific fields don't have machine learning expertise and may find it difficult to train a machine learning model themselves.
Auto machine learning bridges this gap by making it very simple to train a state of the art model. Auto machine learning frameworks like AutoGluon make it such that all you need is a dataset and a few lines of code.
The interactive application that I have developed for Open OnDemand closes the gap further by not requiring the user to write any code at all. Through our application, users can simply choose a dataset and their configuration, and train the model.
The talk will start with a short introduction to Auto Machine Learning and its use cases. I will then provide a short demonstration of the application on the HPRC Launch cluster. Finally I will walk through the challenges of creating an interactive application like this.
