Use sustainable regions for AI/ML training
Description
Training an AI model has a significant carbon footprint. Depending on the model parameters and training iterations, training an AI/ML model consumes a lot of power and requires many servers which contribute to embodied emissions.
Solution
Use a cloud region which has a lower carbon intensity value for running your AI/ML training workloads.
SCI Impact
SCI = (E * I) + M per R
Software Carbon Intensity Spec
Using a lower carbon intensity region for AI/ML training impacts SCI as follows:
E: Using a lower carbon intensity region for ML training would reduce the carbon emissions of ML applications, therefore decreasing the amount of energy consumed.
Assumptions
The migration of workloads to other regions assumes you have taken into consideration privacy, security, or data sovereignty based on your application requirements.
Considerations
Consider the trade-offs between carbon footprint, cost, and latency when selecting a region.