Fairgrounds in action: How the CGIAR forages research team is simplifying machine learning operations

Fairgrounds in action: How the CGIAR forages research team is simplifying machine learning operations

In the Tropical Forages program at the Alliance of Bioversity and CIAT, researchers Andres Ruiz and Juan Andres Cardoso are exploring new frontiers in plant phenotyping: using computer vision to analyze images from drones, satellites, and other remote sensing tools. Their goal is to better understand growth, traits, and environmental responses of forage crops by automating image analysis through machine learning.

However, like many researchers across CGIAR, Andres and Juan have faced a familiar challenge: limited access to computing power and administrator permissions on their computers. “Sometimes we can’t get access to what we need, or some of the tools require administrator permissions we don’t have,” they explained. “It makes it difficult to develop and deploy a full machine learning operations (ML-OPS) pipeline on our computers.”

That’s where Fairgrounds is making a difference. Fairgrounds is designed to provide always-available computing resources on the cloud and to make it easier for teams to build, train, and deploy machine learning models collaboratively. For Andres, this represents a significant step forward.

“With Fairgrounds, the idea is to make everything simpler,” he said. “We can train machine learning models using resources that aren’t limited to our local machines, and we can re- use models we’ve already developed. That’s really important because model development is iterative. You’re always refining and retraining.”

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