MLOps Practices at KNMI: The Collaborative Quantitative Impact Forecasting Use Case

Transitioning from experimental research to operational AI/ML applications is a growing priority in weather and climate. At KNMI, a dedicated MLOps team was established, with the objective of developing the infrastructure and best practices for the implementation of AI/ML models. The resulting setup enables the scalable and reliable delivery of high-quality services to society.

This contribution demonstrates the approach taken through a concrete use case: the Collaborative Quantitative Impact Forecasting (C-QIF) for wildfires. Developed at KNMI in collaboration with the Netherlands Institute for Public Safety (NIPV), C-QIF is a data-driven framework that combines meteorological data with historical wildfire records, providing probabilistic quantitative forecasts of the daily number of wildfires across the Netherlands, up to two weeks in advance. This capability plays a crucial role in enabling decisions on resource allocation and public communication during wildfire events, as well as supporting the professional craftsmanship of operational wildfire experts and first responders.

Originally developed in Octave, C-QIF is being reimplemented in Python and adapted for practical use with cloud-based technologies to enhance scalability and reliability, in order to provide real-time operational services. Data pipelines, model execution, and output delivery are being restructured, with the entire infrastructure deployed on Amazon Web Services (AWS). A self-hosted instance of MLflow, an open-source platform for managing machine learning workflows, is integrated to track model development and ensure reproducibility.

Close collaboration between researchers, engineers, and end users is critical throughout the project. The ongoing interaction ensures alignment between scientific goals and operational needs, and enables the establishment of standard practices for testing, deployment and monitoring — key elements for future model development. This contribution highlights how MLOps capabilities are being built cooperatively, and reflects on the lessons learned in operationalising research models, managing heterogeneous data, and leveraging cloud technologies for AI/ML in practice.