Case Study: ECMWF accelerates weather forecasting innovation with Weights & Biases

ECMWF Accelerates Machine Learning Research in Weather Forecasting With W&B

ECMWF, a leading European weather forecasting center, was exploring machine learning to improve its predictions. Its challenge was managing a large, distributed team of ML engineers who needed to track, compare, and analyze a high volume of concurrent experiments to iteratively develop new models. They required a robust solution for collaboration and data-driven decision-making.

Weights & Biases provided its platform to serve as a central hub for the team's ML efforts. The solution gave everyone visibility into experiments, including hyperparameters and results, which accelerated model iteration. This enabled ECMWF to develop and launch its first entirely ML-based forecasting system, AIFS. Weights & Biases helped the team quickly identify the best models, substantially accelerating their pace of innovation.

ECMWF

Matthew Chantry

Machine Learning Coordinator