arXiv:2601.11046v2 Announce Type: replace Abstract: Machine learning models for climate and Earth science are becoming increasingly capable, yet model deployment into operational use remains a largely unaddressed challenge: general-purpose model-serving tools, such as MLflow and KServe, assume input data availability at the inference node, while data acquisition, failure handling, and preprocessing are trusted to a separate workflow. We present OpFML: Operational Forecasting with Machine Learning - a configurable pipeline integrating the four steps of operational inference into a single TOML-c
The increasing capability of ML models, particularly in complex domains like climate science, highlights the urgent need for robust operational deployment pipelines.
Reliable operational deployment of ML models is crucial for translating scientific breakthroughs into actionable intelligence and real-world applications, especially in critical areas like climate monitoring.
This specialized pipeline addresses the gaps left by general-purpose model-serving tools, integrating the entire operational inference workflow from data acquisition to preprocessing and failure handling.
- · Climate scientists
- · Earth science research institutions
- · AI/ML operations tool developers
- · Organizations relying on operational AI for forecasting
- · General-purpose ML serving platforms (if they don't adapt)
- · Organizations with siloed ML development and operations
- · Legacy forecasting systems
More real-world applications of advanced climate and Earth science ML models become feasible and reliable.
Increased adoption of integrated MLOps practices, leading to more robust and trustworthy AI systems in critical domains.
Potentially accelerated policy-making and resource allocation based on more accurate and timely operational AI insights into environmental changes.
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Read at arXiv cs.LG