Climate action and green software engineering has risen to the top of many technology companies' agenda. With accuracy hungry algorithms ML software is consuming more and more computational resources, largely benefiting from the increasingly better hardware. Are the results worth the environmental cost?

This talk introduces the field of green software engineering, showing options to estimate the carbon footprint and discuss ideas on how to make Machine Learning greener, giving you the tools to take an active part in the climate solution.

Target Audience: Architects, Developers, Data Scientists
Prerequisites: Basic understanding of the AI lifecycle
Level: Advanced

Extended Abstract:
AI systems have a huge carbon footprint and impact our global commitment to keep global warming to no more than 1.5°C – as called for in the Paris Agreement. To reach this goal, emissions need to be reduced by 45 % by 2030 and reach net zero by 2050. The rising interest in getting a better handle on the carbon emissions due to the AI lifecycle has garnered interest from the research and practitioner communities across industry, government, academia, and civil society.

The objective of this learning series is to break beyond surface-level discussions and dive deep into understanding the challenges and opportunities related to assessing and mitigating the carbon impacts of AI systems.

This session will also walk through the Green Software foundation's Software Carbon Intensity specification and explain why how you measure impact matters.