Name: Carbon Aware Computing for GenAI Developers
Brand: DeepLearning.AI
Availability: OnlineOnly
Rating: 4.7 (5 reviews)

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Learner Reviews & Feedback for Carbon Aware Computing for GenAI Developers by DeepLearning.AI

4.7

stars

10 ratings

About the Course

Learn how to perform model training and inference jobs with cleaner, low-carbon energy in the cloud! Learn from Nikita Namjoshi, developer advocate at Google Cloud and Google Fellow on the Permafrost Discovery Gateway, and explore how to measure the environmental impact of your machine learning jobs, and also how to optimize their use of clean electricity. 1. Query real-time electricity grid data: Explore the world map, and based on latitude and longitude coordinates, get the power breakdown of a region (e.g. wind, hydro, coal etc.) and the carbon intensity (CO2 equivalent emissions per kWh of energy consumed). 2. Train a model with low-carbon energy: Select a region that has a low average carbon intensity to upload your training job and data. Optimize even further by selecting the lowest carbon intensity region using real-time grid data from ElectricityMaps. 3. Retrieve measurements of the carbon footprint for ongoing cloud jobs. 4. Use the Google Cloud Carbon Footprint tool, which provides a comprehensive measure of your carbon footprint by estimating greenhouse gas emissions from your usage of Google Cloud. Throughout the course, you’ll work with ElectricityMaps, a free API for querying electricity grid information globally. You’ll also use Google Cloud to run a model training job in a cloud data center that is powered by low-carbon energy. Get started, and learn how to make more carbon-aware decisions as a developer!...

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1 - 5 of 5 Reviews for Carbon Aware Computing for GenAI Developers

By ÖZLEM S

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Jul 30, 2025

I’ve completed the Carbon Aware Computing for GenAI training! Reducing the environmental impact of AI is not just a technological challenge — it's a moral responsibility. Through this training, I gained valuable insights into carbon-aware algorithms, sustainable computing infrastructures, and energy-conscious design. Key takeaways: A deeper understanding of the energy footprint of GenAI systems Practical strategies for reducing carbon emissions in AI development A renewed perspective on how to align innovation with environmental responsibility Technology can — and should — drive a more sustainable future. I'd be happy to connect and discuss this topic further with anyone interested!

By Begüm B

•

Aug 8, 2025

The “Carbon Aware Computing for GenAI Developers” course delivers a well-structured and accessible overview of energy-efficient AI practices. The real-time, hands-on exercises effectively bridge theory and application, allowing immediate experimentation with carbon-aware workload scheduling and optimization strategies. Clear explanations of complex concepts, such as dynamic carbon intensity data integration, make it highly applicable for practitioners seeking to operationalize sustainable AI development.