Sustainable AI: Green Computing and Energy-Aware Models

As Artificial Intelligence becomes more deeply integrated into global systems, the energy and environmental impact of AI models is raising urgent concerns. This course provides a strategic overview of how to develop, deploy, and manage AI systems responsibly—balancing technological advancement with environmental sustainability.

Participants will explore the carbon footprint of AI, energy-efficient model architectures, eco-conscious data practices, and green data centre operations. The course combines theoretical foundations with case studies, technical strategies, and policy discussions to enable a sustainable AI lifecycle.

Section 1: Understanding AI’s Environmental Footprint

• The growth of AI and its energy demands.
• Measuring carbon emissions from AI training and inference.
• Life cycle analysis of AI systems: From data to deployment.
• The role of big data and cloud computing in energy consumption.
• Case study: Carbon footprint of GPT-class models.

Section 2: Principles of Green Computing

• Definition and scope of green computing.
• Low-power computing architectures and edge AI.
• Sustainable software design practices.
• Cloud sustainability and efficient resource provisioning.
• Choosing green cloud providers: AWS, Google, Microsoft benchmarks.

Section 3: Energy-Efficient AI Model Design

• Lightweight model architectures: Distillation, pruning, quantization.
• Trade-offs: Accuracy vs. efficiency vs. latency.
• Techniques for reducing compute intensity.
• Tools for profiling energy usage in model training.
• Lab: Optimize a neural network for lower energy usage.

Section 4: Sustainable Data and Infrastructure Practices

• Eco-aware data acquisition and management.
• Smart data sampling and minimising redundant storage.
• Sustainable data centre infrastructure: Cooling, hardware, energy sources.
• Federated learning and edge deployment for lower central compute.
• Case Study: Sustainability strategy of a global AI company.

Section 5: Policy, Ethics, and Long-Term Responsibility

• Environmental AI governance frameworks.
• Procurement policies that prioritise green tech.
• AI ethics in the context of climate justice and global equity.
• The role of open-source and community-driven sustainable AI.
• Final workshop: Drafting a sustainable AI policy for your organization.

Upon successful completion of this training course, delegates will be awarded a Holistique Training Certificate of Completion. For those who attend and complete the online training course, a Holistique Training e-Certificate will be provided.

Holistique Training Certificates are accredited by the British Accreditation Council (BAC) and The CPD Certification Service (CPD), and are certified under ISO 9001, ISO 21001, and ISO 29993 standards.

CPD credits for this course are granted by our Certificates and will be reflected on the Holistique Training Certificate of Completion. In accordance with the standards of The CPD Certification Service, one CPD credit is awarded per hour of course attendance. A maximum of 50 CPD credits can be claimed for any single course we currently offer.