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AutoML

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Unlocking the potential of machine learning for SMEs by automated machine learni...

Unlocking the potential of machine learning for SMEs by automated machine learning 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 advanc... ver más

31/12/2021

ALU-FR

150K€

Presupuesto del proyecto: 150K€

Líder del proyecto

ALBERTLUDWIGSUNIVERSITAET FREIBURG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Fecha límite participación Sin fecha límite de participación.

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Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2021-12-31

ERC-2019-POC: ERC Proof of Concept Grant Scope:Eligible Countries

I+D

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Este proyecto no cuenta con búsquedas de partenariado abiertas en este momento.

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No hay información privada compartida para este proyecto. Habla con el coordinador.

2 Participantes

ALU-FR

150.00K€ | Lider

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Duración del proyecto: 20 meses Fecha Inicio: 2020-04-07
Fecha Fin: 2021-12-31

Líder del proyecto

ALBERTLUDWIGSUNIVERSITAET FREIBURG No se ha especificado una descripción o un objeto social para esta compañía.

TRL 4-5

Presupuesto del proyecto 150K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto 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.

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