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Posted: 2025-04-13 17:42:49 UTC
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
This article contains some claims that remain unverified. While much of the content may be accurate, exercise care when relying on this information.
Status
Last Updated
2025-04-13 17:43:56 UTC
Verified By
Rollup News
The short course 'Carbon Aware Computing for GenAI Developers' teaches how to reduce the carbon footprint of machine learning models by using real-time data on regional electricity grid carbon intensity, routing model training jobs to low-carbon data centers, measuring carbon footprint with Google Cloud's Carbon Footprint tool, and optimizing training job scheduling.
Quantifying and mitigating the carbon emissions of large machine learning models
Using the ElectricityMaps API for real-time data on regional electricity grid carbon intensity
Routing model training jobs to data centers powered by low-carbon sources
Measuring the carbon footprint of ML activities with Google Cloud's Carbon Footprint tool
Optimizing training job scheduling to use clean energy when it is most abundant
Growing carbon footprint of large machine learning models
Need to quantify and mitigate AI emissions