5 Augmented Reality Careers
November 25, 2024
Article
Recommended experience
Beginner level
Understanding Python aids with coding parts of the lesson, but the concepts help anyone grasp the environmental impact of machine learning workflows.
Recommended experience
Beginner level
Understanding Python aids with coding parts of the lesson, but the concepts help anyone grasp the environmental impact of machine learning workflows.
Use ElectricityMaps API for real-time energy mixes and carbon intensity. Find grids with low-carbon sources like hydro, nuclear, wind, and solar.
Run ML training jobs on cloud servers chosen for low-carbon electricity, based on average and real-time carbon intensity measurements.
Analyze the carbon footprint of Google Cloud usage, including ML training, inference, storage, and API activities.
Only available on desktop
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!
DeepLearning.AI is an education technology company that develops a global community of AI talent. DeepLearning.AI's expert-led educational experiences provide AI practitioners and non-technical professionals with the necessary tools to go all the way from foundational basics to advanced application, empowering them to build an AI-powered future.
Hands-on, project-based learning
Practice new skills by completing job-related tasks with step-by-step instructions.
No downloads or installation required
Access the tools and resources you need in a cloud environment.
Available only on desktop
This project is designed for laptops or desktop computers with a reliable Internet connection, not mobile devices.
Duke University
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