How to estimate and reduce the carbon footprint of machine learning models

The environmental sustainability of machine learning models is increasingly receiving attention, however mostly from academia. Here, the conversation tends to focus on the carbon footprint of language models which are not necessarily representative of the general machine learning field and not enough attention is paid to the operations phase of machine learning models. In addition, existing material on the topic of the environmental impact of machine learning puts too little emphasis on how the environmental impact can actually be estimated and reduced. This article is an attempt to address these issues and is written for practitioners and researchers alike who do hands-on machine learning. Although this post was written with machine learning in mind, some of the contents is also applicable to general software engineering. Before we begin, I want to emphasise that this post was not written to point fingers or moralise. The aim is simply to present information that you may or may not find relevant to your daily activities. All software -- from the apps that run on our phones to the data science pipelines that run in the cloud -- consume electricity and as long as not all our electricity is generated by renewable energy sources, electricity consumption will have a carbon footprint.