Sustainable AI - From Impact to Implementation

Learn about the environmental footprint of AI beyond data center energy consumption

Discover how data center location, electricity mix, and cooling infrastructure affect the environmental cost of running AI.

Understand the difference between training emissions (one-time) and inference emissions (ongoing), and why both matter.

Learn foundational principles to guide your decisions about which AI approaches to use, when to use them, and when alternatives are better.

Apply sufficiency principles: when is AI the right tool, and when are simpler, less energy-intensive solutions more appropriate?

Understand tradeoffs between model size, accuracy, and energy consumption to make informed choices for your use case.

Quantify AI's environmental impact using open source tools and use case based approach.

Understand key metrics (KPIs) and parameters to drive decision-making.

Learn methodologies to estimate carbon footprint when direct measurement isn't possible, using available data and benchmarks.

Explore practical techniques to reduce AI's carbon footprint—from architecture choices to model selection to efficient deployment.

Discover how choosing the right model can cut carbon emissions by orders of magnitude.

Master inference optimization techniques that reduce ongoing energy use.

Learn to translate carbon metrics into cost savings and business benefits that justify sustainable AI investments to leadership.

Build business cases for sustainable AI strategies and navigate the regulatory landscape shaping environmental requirements for AI.

Understand emerging regulations that affect how organizations must measure and report AI impact.

Build actionable proposals integrating measurement, optimization, and strategy into a concrete plan you can apply in your role.

Design a measurement strategy tailored to your organization's systems, data availability, and reporting requirements.

Create a prioritized roadmap of optimization initiatives with timelines, resource requirements, and expected impact.