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Duration 21 hours
Course Outline
Introduction to Energy-Efficient Artificial Intelligence
- The imperative of sustainability in artificial intelligence initiatives
- Analysis of energy consumption patterns in machine learning operations
- Illustrative examples of energy-efficient AI deployments for government agencies
Compact Model Architectures
- Assessing model scale and architectural complexity
- Strategies for engineering compact yet high-performing models for government use
- Comparative analysis of model architectures focused on resource efficiency
Optimization and Compression Techniques
- Application of model pruning and quantization methods
- Leveraging knowledge distillation to enhance model compactness
- Adoption of efficient training protocols to minimize energy expenditure
Hardware Considerations for AI
- Selection of energy-efficient hardware infrastructure for training and inference workloads
- Evaluation of specialized processors such as TPUs and FPGAs for government deployment
- Optimization of the trade-off between computational performance and power consumption
Green Coding Practices
- Development of energy-conscious code standards
- Profiling and refinement of AI algorithms for optimal resource utilization
- Establishment of sustainable software development best practices for government systems
Renewable Energy and AI
- Integration of renewable energy sources into AI operational frameworks
- Sustainability measures for data center operations
- Strategic planning for green AI infrastructure in public sector contexts
Lifecycle Assessment of AI Systems
- Quantification of the carbon footprint associated with AI model deployment
- Implementation of strategies to mitigate environmental impact across the AI lifecycle
- Documentation of lifecycle assessment case studies relevant to public sector operations
Policy and Regulation for Sustainable AI
- Review of global standards and regulatory frameworks governing AI sustainability
- Examination of policy mechanisms that promote energy-efficient AI adoption
- Assessment of ethical considerations and societal implications for government stakeholders
Project and Assessment
- Construction of a prototype utilizing small language models within a specified domain for government application
- Formal presentation of the energy-efficient AI system design
- Evaluation based on technical proficiency, innovative approach, and environmental contribution
Summary and Next Steps
Requirements
- A comprehensive understanding of deep learning concepts
- Proficiency in Python programming
- Practical experience with model optimization techniques
Target Audience
- Machine learning engineers
- AI researchers and practitioners
- Environmental specialists within the technology sector
