Sustainable AI for Developers and Practical Carbon Reduction: A Conversation with Dr. Sasha Luccioni

Explore the hidden environmental footprint of AI systems in this conversation with Sasha Luccioni, AI and Climate Lead at Hugging Face. Learn how AI data centers impact local communities and why measuring AI's energy and water use is so crucial as the technology scales. Sasha covers when to choose AI or opt for simpler solutions to ensure sustainability, as well as how to leverage practical strategies to reduce AI's environmental footprint with tools like CodeCarbon and techniques such as model distillation and quantization. Along the way, explore how to integrate sustainability into your everyday development workflow—avoiding defensive rhetoric in favor of actionable solutions. An ideal fit for developers, ML engineers, and technical project leads, this course equips you with the skills you need to make a tangible impact by building AI systems responsibly and sustainably.

Learning objectives
Measure AI emissions using CodeCarbon and establish baseline metrics for your projects.
Optimize model efficiency through right-sizing, distillation, quantization, and other practical reduction techniques.
Evaluate and select AI models and APIs using emerging sustainability standards and carbon disclosure criteria.
Integrate sustainability practices into your development lifecycle with transparency, reporting, and decarbonization strategies.