We may earn an affiliate commission when you visit our partners.
Course image
Nikita Namjoshi

Learn how to perform model training and inference jobs with cleaner, low-carbon energy in the cloud!

Read more

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!

Enroll now

What's inside

Syllabus

Carbon Aware Computing for GenAI Developers
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!

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Suitable for GenAI developers seeking to reduce the environmental impact of their machine learning workflows
Provides practical guidance on optimizing energy consumption and carbon footprint
Empowers developers with real-time electricity grid data for informed decision-making
Utilizes the Google Cloud Carbon Footprint tool for comprehensive carbon footprint estimation
Course advised by Nikita Namjoshi, a recognized Google Cloud developer advocate
Instructs on querying real-time electricity grid data and selecting regions with low carbon intensity

Save this course

Save Carbon Aware Computing for GenAI Developers to your list so you can find it easily later:
Save

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Carbon Aware Computing for GenAI Developers with these activities:
Review prerequisites
As a refresher, review the basics of machine learning and cloud computing, including basic concepts, algorithms, and tools.
Browse courses on Machine Learning
Show steps
  • Review key machine learning concepts (e.g., supervised learning, unsupervised learning, model evaluation).
  • Review key cloud computing concepts (e.g., cloud architecture, virtual machines, data storage).
Explore Google Cloud Carbon Footprint tool
Follow a tutorial or documentation to get familiar with the features and functionality of the Google Cloud Carbon Footprint tool.
Show steps
  • Complete the tutorial: https://cloud.google.com/carbonfootprint/docs/quickstart
Practice querying real-time electricity grid data
Reinforce your understanding of how to query real-time electricity grid data using the ElectricityMaps API.
Show steps
  • Visit the ElectricityMaps website: https://electricitymaps.com/
  • Explore the map and query real-time electricity grid data for different regions.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Blog post: Best practices for low-carbon ML training
Synthesize your knowledge by creating a blog post or article that shares best practices for reducing the carbon footprint of machine learning training jobs.
Show steps
  • Research and gather information on low-carbon ML training.
  • Identify and outline key best practices.
  • Write and edit the blog post, ensuring clarity and conciseness.
Mentor a junior developer
Deepen your understanding by sharing your knowledge and mentoring a junior developer interested in carbon-aware computing.
Show steps
  • Identify a junior developer who is interested in learning about carbon-aware computing.
  • Set up regular meetings to provide guidance and support.
  • Share resources and materials to help the developer learn and grow.
Participate in a carbon-aware ML hackathon
Challenge yourself to apply your skills and knowledge in a practical setting by participating in a hackathon focused on carbon-aware ML.
Show steps
  • Find an appropriate hackathon or competition.
  • Form a team and develop a project proposal.
  • Implement and refine your solution.
Contribute to an open-source project on carbon-aware computing
Contribute to the open-source community by collaborating on a project that promotes carbon-aware computing.
Show steps
  • Identify a suitable open-source project focused on carbon-aware computing.
  • Review the project documentation and identify areas where you can contribute.
  • Submit code contributions or documentation improvements.

Career center

Learners who complete Carbon Aware Computing for GenAI Developers will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.

Share

Help others find this course page by sharing it with your friends and followers:

Similar courses

Here are nine courses similar to Carbon Aware Computing for GenAI Developers.
Energy Demand in Buildings
Most relevant
Why Move Towards Cleaner Power
Most relevant
Renewable Power and Electricity Systems
Most relevant
Sustainability & Technology: Executive Briefing
Most relevant
Incorporating Renewable Energy in Electricity Grids
Most relevant
Energy Supply Systems for Buildings
Most relevant
The Sustainability Imperative
Most relevant
Designing Climate-Neutral Buildings and Transport
Most relevant
Net-Zero Building Fundamentals
Most relevant
Our mission

OpenCourser helps millions of learners each year. People visit us to learn workspace skills, ace their exams, and nurture their curiosity.

Our extensive catalog contains over 50,000 courses and twice as many books. Browse by search, by topic, or even by career interests. We'll match you to the right resources quickly.

Find this site helpful? Tell a friend about us.

Affiliate disclosure

We're supported by our community of learners. When you purchase or subscribe to courses and programs or purchase books, we may earn a commission from our partners.

Your purchases help us maintain our catalog and keep our servers humming without ads.

Thank you for supporting OpenCourser.

© 2016 - 2024 OpenCourser