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.
