Machine learning operations for weather applications

In weather applications, machine learning is emerging as an innovative technology with the potential to address many of the shortcomings of traditional modelling procedures. The trend is fostered by the growing availability of observational data, computational resources, and high-level software libraries. However, to ensure that machine learning can deliver on its promises, build trust, and eventually transition to become a reliable technology for production, it is also important to consider the technical and engineering challenges that arise when introducing machine learning in an operational environment.

MLOps is the set of practices that aims at deploying and maintaining machine-learning models in production continuously, reliably, and efficiently. At the core of MLOps is the idea that models can easily change, while the underlying workflows remain. In this sense, the emphasis is shifted from training a specific ML model to building an integrated ML system and to continuously operate it in production.

We will present our first experiences with MLOps for weather forecasting applications at MeteoSwiss. As an example, we will use a ML-based model for postprocessing NWP surface wind forecasts, as it covers the most common and relevant challenges, including the need for efficient data loading and manipulation, the monitoring and visualization of prediction quality, and the automation of model training and deployment pipelines. In this contribution, we aim at sharing our endeavor for MLOps best practices in the applied context of a national meteorological service with the hope to foster discussion and exchanges on the topic of machine learning operations for meteorological applications and their transition to the cloud.