Project description
Easy, affordable access to the most advanced machine learning methods
Today’s business managers base their decisions on an enormous amount of reliable data integrated into business processes and customer demands. Machine learning (ML) has become the most helpful technology, especially for smart, data-driven industries enabling automatisation of many of those processes. However, SMEs lack the necessary expertise for customising ML methods. For this reason, the EU-funded AutoML project will prove an affordable, automated machine learning (AutoML) method to enable efficient implementing of most advanced ML applications. The goal will be to automatically elaborate and use the user’s data. The AutoML will use a prototype developed by the ERC-funded BeyondBlackbox project. It will be adapted into a professional prototype for implementation in an industrial setting.
Objective
Machine learning has become a key technology for modern data-driven industrial applications. This success is built on recent research advances in the field of artificial intelligence and more specifically was enabled by key advances in machine learning. Unfortunately, the performance of many machine learning methods is very sensitive to a myriad of design decisions and thus requires a significant amount of machine learning expertise which is often rare and makes this technology inaccessible for small and medium-sized companies that cannot afford their own team of machine learning experts. My ERC grant BeyondBlackbox on automated machine learning (AutoML) addresses this problem from a research perspective. In it, my team and I developed methods which systematically and efficiently adapt and tune machine learning pipelines and implemented them into a research prototype. This resulting research prototype, in principle, allows ML novices easy and affordable access to the most advanced ML methods, automatically customized for the user's own data, and with this research prototype, my team and I have won several competitions, including competitions against up to 130 teams of human ML experts. The potential economic impact is substantial since AutoML technology saves computational resources and human time and therefore reduces the cost of creating value from ML. In this POC project, I and my team will transform our existing research prototype to a professional prototype, perform a technical validation, perform market research and build up business contacts to evaluate this prototype in an industrial setting. Furthermore, we will develop a sustainable business model and assess ways of commercializing the advances made in my ERC grant in order to bring them to market.
Fields of science
Programme(s)
Funding Scheme
ERC-POC-LS - ERC Proof of Concept Lump Sum PilotHost institution
79098 Freiburg
Germany