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By knowing the carbon emissions of the energy that powers your applications,
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you and your organisation can leverage greener energy sources to reduce your CO2 emissions by:
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By knowing the carbon emissions of the energy that powers your applications, you and your organisation can leverage greener energy sources to reduce your CO2 emissions by:
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* Building AI models when carbon emissions are lower
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* Deploying software into the cloud in locations that have greener energy sources
@@ -80,14 +79,10 @@ deployment in the greenest location.
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The Carbon Aware SDK is being used by large and small companies around the
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world. Some of the world’s biggest enterprises and software companies, through
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to start-ups.
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Both UBS and Vestas have used the SDK, with further details over on the [adopters page](./docs/adopters.md).
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to start-ups. Both UBS and Vestas have used the SDK, with further details over on the [adopters overview](./docs/adopters.md).
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Machine Learning (ML) workloads are a great example of long running compute
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intensive workloads, that often are also not time critical. By moving these
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workloads to a different time, the carbon emissions from the ML training can be
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reduced by up to 15%, and by moving the location of the training this can be
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intensive workloads, that often are also not time critical. By moving these workloads to a different time, the carbon emissions from the ML training can be reduced by up to 15%, and by moving the location of the training this can be
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reduced even further, at times by up to 50% or more.
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## What does the SDK/API provide that 3rd party data providers such as WattTime or ElectricityMaps do not?
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