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DTSTART;TZID=Europe/Paris:20230926T170000
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DTSTAMP:20260512T042125
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SUMMARY:WORKSHOP - Continuous estimation of the environmental impact of Machine Learning solutions : methodology\, best practices and tools
DESCRIPTION:Inscription\n                \n            \n            \n			\n				\n				\n				\n				\n				Machine Learning prerequisites* : Basic knowledge in ML/Data/IA \nPython prerequisites* : Basic knowledge in Python \nTechnical prerequisitesNone \nSpeakersThis workshop will be presented by Samuel Rincé\, Lead Data Scientist at Alygne & OS Contributor at Boavizta \nWorkshop overview \nAlthough digital technologies currently represent about 2.1-3.9% of greenhouse gas emissions worldwide\, they are likely to double by 2030. Thus\, in a world where carbon emissions\, energy and water usage are becoming major concerns\, rethinking how we develop IT projects\, such as AI and ML projects\, to aim at reducing our digital environmental footprint is a real challenge. \nHowever\, there is no framework to take these issues into account.How can we integrate environmental footprint assessment into AI projects?From development to production\, how can we manage environmental impacts?What tools are needed to simplify and automate measurement? \nAfter this workshop\, you’ll be able to think about more sustainable approaches to your AI projects.  \nCourse of the workshop  \n\nMethodology for environmental impact estimation\nImpact evaluation tools in the development phase\nValidation and ecological benchmarking in CI\nImpact monitoring in production\nPractical project use cases
URL:https://datacraft.paris/event/continuous-estimation-of-the-environmental-impact-of-ml-solutions/
LOCATION:datacraft –\, 3 rue Rossini\, 75009 Paris\, France
CATEGORIES:- Event in English -
ORGANIZER;CN="datacraft":MAILTO:contact@datacraft.paris
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