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WORKSHOP – Continuous estimation of the environmental impact of Machine Learning solutions : methodology, best practices and tools
Machine Learning prerequisites
* : Basic knowledge in ML/Data/IA
* : Basic knowledge in Python
This workshop will be presented by Samuel Rincé, Lead Data Scientist at Alygne & OS Contributor at Boavizta
Although 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.
However, 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?
After this workshop, you’ll be able to think about more sustainable approaches to your AI projects.
Course of the workshop
- Methodology for environmental impact estimation
- Impact evaluation tools in the development phase
- Validation and ecological benchmarking in CI
- Impact monitoring in production
- Practical project use cases