Short Course

Carbon Aware Computing for GenAI Developers

In Collaboration With

Google Cloud



1 Hour


Nikita Namjoshi

  • Retrieve real-time data on global energy mixes and carbon intensity from the ElectricityMaps API. Identify power grids that produce electricity from low-carbon sources, such as hydro, nuclear, wind, and solar power.

  • Run a machine learning training job using low-carbon electricity by re-directing training tasks to cloud server locations selected based on their average and real-time carbon intensity measurements.

  • Analyze the carbon footprint of sample Google Cloud usage data, including machine learning training, inference, storage, and other API activities.

What you’ll learn in this 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. 

  • 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).
  • 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.
  • Retrieve measurements of the carbon footprint for ongoing cloud jobs.
  • 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!

Who should join?

Familiarity with Python will help with the coding parts of the lesson, although the concepts covered can help anyone gain an understanding of the environmental impact of machine learning workflows.


Nikita Namjoshi

Nikita Namjoshi


Developer Advocate at Google Cloud

Course access is free for a limited time during the DeepLearning.AI learning platform beta!

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