🌿 Green AI: Designing Energy-Efficient AI Systems

To equip AI engineers, researchers, and product developers with the knowledge and skills to design, develop, and deploy energy-efficient and sustainable AI systems, promoting responsible innovation that aligns with environmental goals and carbon reduction targets.

• Raise awareness about the carbon footprint of AI systems

• Provide hands-on skills for building and deploying efficient AI models

• Foster cross-disciplinary knowledge bridging AI, hardware, and sustainability

• Encourage responsible innovation aligned with global climate goals

• Equip participants to become advocates of Green AI in their organizations

Day 1: Introduction to Green AI and the Carbon Footprint of AI

• What is Green AI?

  • Defining Green AI and its importance in sustainable technology development.

• Carbon Footprint of AI

  • Analyzing the energy consumption and carbon emissions associated with AI technologies, with a global context.

• Challenges and Opportunities

  • Identifying the challenges and opportunities in reducing AI’s environmental impact across industries.

• Ethical and Policy Considerations

  • Exploring the ethical considerations and policy frameworks supporting sustainable AI practices.

Day 2: Efficient Algorithm Design & Edge AI for Low-Carbon Computing

• Efficient Algorithm Design

  • Techniques for designing algorithms that minimize energy consumption without sacrificing performance.

• Edge AI and Low-Carbon Computing

  • The role of Edge AI in reducing carbon emissions by decentralizing computation and enabling low-carbon solutions.

• Case Studies

  • Real-world applications of energy-efficient AI across various industries.

• Designing for Sustainability

  • Approaches to incorporating sustainability from the initial design phase through deployment.

Day 3: Evaluation Tools & Dashboard Reporting on AI Sustainability Metrics

• Introduction to Evaluation Tools

  • Overview of tools and methods for evaluating AI’s environmental impact and sustainability.

• Using CodeCarbon

  • Practical guidance on integrating CodeCarbon to track and optimize AI’s carbon footprint.

• MLCO2 and Other Tools

  • Utilizing MLCO2 and other platforms to assess and reduce the carbon emissions of AI systems.

• Dashboard Reporting

  • Setting up dashboards to monitor and visualize AI sustainability metrics in real time.

• Continuous Monitoring

  • Best practices for continuous monitoring and ongoing optimization of AI sustainability efforts.

• Understand the environmental impact of AI model development and deployment

• Apply model compression and efficiency techniques to reduce carbon emissions

• Analyze and benchmark energy consumption across AI pipelines

• Integrate sustainable practices into AI workflows and corporate strategies

• Earn a certification in energy-efficient AI development and deployment

• AI engineers and machine learning practitioners

• Data scientists and software developers

• Sustainability managers and ESG officers

• Cloud architects and DevOps engineers

• Students (UG/PG/PhD) in AI, data science, or green computing