22 au 24 février, 2023
Montréal, Canada

Measure carbon emissions of ML projects, in python

The environmental cost of ML has been increasing in recent years, due to its ever-increasing adoption in many software applications, as well as their size and complexity. Many parameters impact the carbon cost of a python program, such as the hardware used and the location of its execution. In this talk, we present the internals of Code Carbon, a library developed to better estimate the impact of ML projects, throughout their global life cycle.

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Amine Saboni


As a Data Engineer, I am enthusiastic about building ML systems which can provide robust and responsible ML services. After 4 years working as a consultant, I joined DiliTrust to focus on ML operations and scaling the ML training and inference system.

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