Synthèse de code pour analyse prédictive efficace et passant à l'échelle

par Amela Fejza

Projet de thèse en Informatique

Sous la direction de Pierre Geneves.

Thèses en préparation à Grenoble Alpes , dans le cadre de École doctorale mathématiques, sciences et technologies de l'information, informatique (Grenoble) , en partenariat avec Laboratoire d'Informatique de Grenoble (laboratoire) depuis le 01-10-2018 .


  • Résumé

    Building AI applications with amounts of data that exceed single computer capa- bilities remains a very time-consuming and expensive task. As pointed out in a recent Stanford report [2], “This expense comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring”. We seek to develop new programming methods, abstractions and tech- niques to synthesize code executed by big data frameworks. We want to generate correct and efficient distributed code, for the purpose of facilitating the construction of big data and AI applications.

  • Titre traduit

    Code synthesis for scalable and efficient predictive analytics


  • Résumé

    Building AI applications with amounts of data that exceed single computer capa- bilities remains a very time-consuming and expensive task. As pointed out in a recent Stanford report [2], “This expense comes not from a need for new and improved statistical models but instead from a lack of systems and tools for supporting end-to-end machine learning application development, from data preparation and labeling to productionization and monitoring”. We seek to develop new programming methods, abstractions and tech- niques to synthesize code executed by big data frameworks. We want to generate correct and efficient distributed code, for the purpose of facilitating the construction of big data and AI applications.