As AI/ML technologies become more prevalent, organizations must monitor their carbon footprints throughout the ML lifecycle to ensure sustainability. This includes tracking operational emissions during model training and inference, as well as lifecycle emissions from hardware manufacturing. Common challenges include a lack of standardized energy measurement methods. To mitigate environmental impacts, organizations can adopt best practices such as optimizing models, selecting efficient hardware, and utilizing cloud platforms like AWS and GCP, alongside tools like CodeCarbon and MLCarbon for energy consumption reduction.
For organizations using AI/ML technologies, it is crucial to systematically track the carbon footprint of ML lifecycle and implement best practices in model development and deployment stages.
There are open-source tools like CodeCarbon and MLCarbon to track and reduce energy consumption. Cloud platforms such as Google Cloud Platform (GCP) and Amazon Web Services (AWS) enable sustainability in AI workloads.
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