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❓What if building the next breakthrough in AI didn’t have to cost the Earth?
❓What if you could design powerful models that run faster, cheaper, and greener-all while future-proofing your career?
Sustainable Computing for Green Labs: Designing Energy-Efficient AI Models with 25 Hands-On Exercises is your practical roadmap to mastering eco-friendly AI. Written for programmers, data scientists, and researchers, this book combines cutting-edge theory with 25 guided projects that take you step-by-step from energy measurement to building fully optimized green AI systems.
Inside, you’ll discover:
How AI consumes energy-and where the biggest hidden costs lie in data pipelines, training, and inference.
Proven efficiency strategies-from pruning and quantization to lightweight architectures and federated learning.
Green hardware and cloud setups-make the right choices for maximum performance with minimum footprint.
Ethical and lifecycle insights-understand carbon accounting, fairness, and long-term sustainability.
25 portfolio-ready projects-complete exercises with real code, datasets, and tools like TensorFlow, PyTorch, and CodeCarbon.
Whether you’re setting up a personal green lab, preparing for the future of AI jobs, or seeking to align your work with environmental goals, this book gives you the skills to build models that are not only smarter-but responsible.
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