DeepLearning.AI
Learn how to perform model training and inference jobs with cleaner, low-carbon energy in the cloud. This course will guide you through measuring the environmental impact of your machine learning jobs and optimizing their use of clean electricity. Enroll now to make more carbon-aware decisions as a developer!
In this course, you will learn to retrieve real-time data on global energy mixes and carbon intensity from the ElectricityMaps API. You'll identify power grids that produce electricity from low-carbon sources and run machine learning training jobs using low-carbon electricity. The course will also cover analyzing the carbon footprint of Google Cloud usage data and using the Google Cloud Carbon Footprint tool. By the end, you'll be equipped to make more sustainable decisions in your development processes.
Developers
Individuals interested in learning how to perform model training and inference jobs with cleaner, low-carbon energy in the cloud.
Machine Learning Enthusiasts
Those who want to understand the environmental impact of machine learning workflows and optimize their use of clean electricity.
Sustainability Advocates
People looking to make more carbon-aware decisions in their technology usage and development processes.
This course offers key benefits such as understanding the environmental impact of machine learning workflows and optimizing them for cleaner energy use. It covers essential topics like carbon intensity analysis and Google Cloud usage, making it ideal for developers and sustainability advocates. Enhance your skills and contribute to a more sustainable future.
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Familiarity with Python will help with the coding parts of the lesson.
Basic understanding of machine learning concepts is beneficial.
Interest in environmental sustainability and clean energy solutions.
Nikita Namjoshi
Developer Advocate, Google Cloud
Nikita Namjoshi is a developer advocate at Google Cloud and a Google Fellow on the Permafrost Discovery Gateway.
Cost
Free
Duration
Dates
Location
