Earth Day. Explaining Green Machine Learning
Machine learning and (especially) deep learning have become increasingly more accurate in recent years. This has improved our lives in ways we couldn’t have imagined. As the AI revolution continues to evolve, more improvements are on the way.
The AI revolution isn’t without costs, though. The additional infrastructure needed to support AI has a negative impact on our environment. As models get more accurate, they typically emit more CO2; in fact, a 1% boost in model accuracy often means a 10x increase in model size and related CO2 emissions. As expected, these are some of the undesirable side-effects of the AI revolution.
Join our experts as they discuss:
- How we can make AI “green” by reducing CO2 emissions.
- How selecting the correct use cases and deploying the right models can make AI more efficient and accurate.
- Oskar Eriksson (DataRobot, Customer Facing Data Scientist)
- Jack Jablonski (DataRobot, AI Success Manager)
After watching the learning session, check out this DataRobot article: Moving from Red AI to Green AI, Part 1: How to Save the Environment and Reduce Your Hardware Costs
Also, search the DataRobot public documentation for: