Green AI: Making Machine Learning Environmentally Sustainable

Abstract

After discussing the significant carbon footprint of AI, Charles will offer practical strategies to reduce environmental impact at each stage of the AI lifecycle. These strategies include using smaller datasets, transfer learning, model compression techniques and edge computing.

Slides

Resources

Data sources:
IEA Data Centres and Data Transmission Networks
Microsoft sustainability report
Google sustainability report
Hockey stick
Putting a CO2 price on computation

Tools:
Electricity Maps
WattTime
AQT
ML.Energy Leaderboard

Techniques:
Demand shifting and shaping
Federated Learning
AI Consumes Lots of Energy. Can It Ever Be Sustainable?
Speculative Decoding

Books:
The Developer's Guide to Cloud Infrastructure, Efficiency and Sustainability by Charles Humble
Kubernetes at the Edge: Container Orchestration at Scale by Charles Humble
Building Green Software by Anne Currie, Sarah Hsu, Sara Bergman
Not the End of the World by Hannah Richie

Charles Humble

Techie, podcaster, editor, author and consultant

Charles is available to give these talks at conferences and events.