Descrizione del progetto
Accesso facile e conveniente ai più avanzati metodi di apprendimento automatico
I moderni manager aziendali basano le proprie decisioni su un’enorme quantità di dati affidabili integrati nei processi aziendali e nelle richieste dei clienti. L’apprendimento automatico (ML, Machine Learning) è diventato la tecnologia più utile, principalmente per le industrie intelligenti basate sui dati che consentono l’automazione di molti di questi processi. Le PMI non hanno tuttavia le competenze necessarie per personalizzare i metodi di ML. Per questo motivo, il progetto AutoML, finanziato dall’UE, dimostrerà un metodo economico e automatico di apprendimento automatico (AutoML, Automated Machine Learning) per consentire l’implementazione efficiente della maggior parte delle applicazioni ML avanzate. L’obiettivo sarà quello di elaborare e utilizzare automaticamente i dati dell’utente. AutoML utilizzerà un prototipo sviluppato dal progetto BeyondBlackbox finanziato dal CER, adattandolo in un prototipo professionale per l’implementazione in un ambiente industriale.
Obiettivo
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.
Campo scientifico
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Meccanismo di finanziamento
ERC-POC-LS - ERC Proof of Concept Lump Sum PilotIstituzione ospitante
79098 Freiburg
Germania