ease.ml: integrating AutoML into SDSC Eco-Systems

Co-PIs:

  • Prof. Dr. Ce Zhang, ETH Zürich
  • Prof. Andreas Krause, ETH Zürich

 

Objectives:

  • Integrate ease.ml into the SDSC eco-system (in particular, the RENKU platform) and provide model selection functionalities to a subset—mainly deep-learning based applications over images, text, and time series—of SDSC applications;
  • Work closely with the SDSC to further advance the state of the art of AutoML, including overcoming a number of computer science challenges that mainly involve developing novel techniques for scalable Bayesian optimization;
  • Pursue the emerging direction of how to automatically manage ML pipelines in a data-driven fashion.